Computational measures of linguistic variation: A study of Arabic varieties

COMPUTATIONAL MEASURES OF LINGUISTIC VARIATION: A STUDY OF ARABIC VARIETIES BY MAHMOUD ABEDEL KADER ABUNASSER DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Linguistics in the Graduate College of the University of Illinois at Urbana-Champaign, 2015 Urbana, Illinois Doctoral Committee: Professor Elabbas Benmamoun, Chair Professor Mark Hasegawa-Johnson, Co-Chair Professor Ryan Shosted Professor Eiman Mustafawi i  ABSTRACT This thesis introduces and discusses a new methodology for measuring the variation between linguistic varieties. I compare five Arabic varieties – Modern Standard Arabic MSA, Gulf Arabic GA, Levantine Arabic LA, Egyptian Arabic EA, and Moroccan Arabic MA – considering both lexical and pronunciation variation. I introduce the idea of measuring the amount of linguistic variation asymmetrically; the amount of linguistics variation between a speaker of variety A and a hearer of variety B is not necessarily equal to the amount of linguistic variation between a speaker of variety B and a hearer of variety A. I propose a new mathematically based computational representation of sound that enables the incorporation of phonetic features and articulatory gestures in measuring the amount of pronunciation variation. I also implement an optimization technique to assign weights and parameters to the phonetic features and articulatory gestures for the proposed representation of sound. The developed methodology, tools and techniques lead to a better understanding of the structure of language and have implications for both theoretical linguistics and applied work in natural language processing NLP, it both provides a computational technique to assess the plausibility of defining the components of sound and opens a new venue to the possibility of utilizing a representation of sound that is phonetically motivated and computationally applicable to NLP problems. This research could potentially yield insights into the issues of mutual intelligibility between Arabic varieties and dialect identification. Measuring lexical and pronunciation variation is based on native speaker elicitations of the Swadesh list for the local varieties of Arabic; MSA is represented by data from dictionaries. The data collection procedure allows the participants to provide more than one translation. I also provide a context sentence for all lexical items to rule out cases of ambiguity. The amount of ii lexical variation is measured at two levels of representation: the word level and the phonemic level. At the word level, the amount of linguistic variation is based on whether the words share a linguistic origin. The phonemic level, using IPA transcription of words, looks at more details in measuring the lexical variation. The amount of pronunciation variation is measured at three levels. The first and most abstract level is the phonemic level. The second incorporates the mathematical representation of sound; which encodes phonetic features and articulatory gestures. The third allows the vowels to be represented non-categorically based on the values of the first and second formant frequencies, MSA is not included at this level. The results of the measures of linguistic variation developed in this study confirm two observations about the communication between speakers of the Arabic varieties and provide an answer for the frequently asked question about the closeness of the Arabic varieties to each other. The first observation is that MA seems to be relatively distant from the other local varieties (GA, EA, and LA) than those varieties are from each other, which relates to the geographical distances between those varieties. The second observation is the asymmetric pattern of intelligibility in the communication of EA speakers with the members of the other local varieties; GA, LA, and MA speakers seem to understand EA speakers better than the EA speakers understand them. This asymmetric pattern of intelligibility is reflected by the variation metrics developed in this research. As for the closeness of the local varieties to MSA, GA and – to some extent – LA seem to be the closest, followed EA, and MA is the farthest. In addition, EA seems to be closer to MA than both LA and GA. Moreover, EA speakers are closer to LA hearers than GA hearers. On the other hand, GA speakers are closer to LA hearers than EA hearers. Finally, the last measure, that of pronunciation variation, situates LA speakers closer GA hearers than EA hearers. iii  ACKNOWLEDGEMENTS Thanks are due to God for his endless blessings upon me. My profound gratitude is to those who have supported and helped me throughout my education. Among the many people who have helped me, two have provided help during my graduate studies beyond what is expected from them: my advisor Elabbas Benmamoun and my friend and colleague Tim Mahrt. Without the help I have received from them, I would have not accomplished this. I would like to thank them for their generous help, patience, support, and guidance. I am forever grateful to Mark Hasegawa-Johnson for his support and for his flexibility which allowed me to craft what I consider the most important piece in this work: the linguistically motivated, mathematically grounded, and computationally effective representation of sound. No doubt, Ryan Shosted provided great insight in the area of phonetics. I was able to better flesh out a non-categorical representation for vowels with his valuable feedback and comments. I would like to extend my gratitude to Eiman Mustafawi for the well-thought and accurate questions and comments about the representation of Arabic varieties and the transcription procedure; sharing her expertise was a great help for my research. In addition to my respected committee members, I have discussed ideas related to the research of this dissertation with other professors. I would like to thank Chilin Shih, José Hualde, Jennifer Cole, and Mona Diab for their valuable feedback. Most importantly, my deep appreciation goes to my wonderful parents who raised me in my childhood and are still caring, loving, and supportive. I would have certainly not be where I am now without their sincere devotion. And my beloved wife Halema is the one that shared with me every single moment in this journey. Halema has made my success throughout graduate school possible with her love, support, and determination. My wonderful children, Ahmed, Mira, iv and Yamen were the biggest motivation during my studies. I thank my sister Eman for encouraging me the most to pursue a PhD degree. My brother Ahmad has always been supportive. I thank him for all the useful discussions and for always standing by my side in this long journey. I also would like to thank my brother Mohammad who have helped me better understand optimization techniques used in Engineering. My sister Enas has been supportive and encouraging. I thank her for everything she did for me and for my family. I am in debt to the participants who gave their time and patience to complete the required recordings for this research. I am also grateful to my friends Abdelaadim Bidaoui and Iftikhar Haider for their support and help. I will miss our conversations and the tea we had together on an almost daily basis. I am also grateful to my friend Moad Hajjam and my niece Raneem Saadah for making drawings for one of one of the pilot studies I used to prepare for this research. I also would like to thank Daniel Ross for his willingness to review the manuscript. His help enabled a better flow of the ideas in addition to correcting spelling and grammatical mistakes. I owe Tim Cunningham my gratitude for the excellent IT support he has provided throughout my stay at the U of I. I gratefully acknowledge Qatar National Research Fund (QNRF) grant NPRP-09-410-1- 069 (M. Hasegawa-Johnson, PI) and National Science Foundation (NSF) grant BCS-0826672 (E. Benmamoun, PI) for partially funding this research. v  TABLE OF CONTENT CHAPTER 1: INTRODUCTION AND OVERVIEW ................................................. 1 CHAPTER 2: PARTICIPANTS, DATA SOURCES, AND DATA COLLECTION PROCEDURE ............................................................................................................... 8 2.1 Participants and MSA Data Sources ................................................................. 9 2.2 Swadesh list ...................................................................................................... 10 2.3 Allowing Multiple Translations ........................................................................ 13 2.4 Data collection procedure and tools .................................................................. 14 2.4.1 BrowseHTMList application ................................................................... 15 2.4.2 Synchronizing the timestamps and TextGrid boundaries ........................ 19 2.5 Data segmentation ............................................................................................. 20 2.6 Transcription ..................................................................................................... 22 2.7 Predicting vowel landmarks .............................................................................. 25 2.8 The non-categorical representation of vowels .................................................. 38 CHAPTER 3: MEASURE OF LEXICAL VARIATION BASED ON THE PERCENTAGE OF NON-COGNATE WORDS ......................................................... 42 CHAPTER 4: MEASURES OF LEXICAL AND PRONUNCIATION VARIATION BASED ON PHONE STRINGS ........................................................... 49 4.1 Measure of lexical variation at the phonemic level .......................................... 53 4.2 Measure of Pronunciation variation at the phonemic level .............................. 58 CHAPTER 5: MEASURES OF PRONUNCIATION VARIATION BASED ON THE MATHEMATICAL REPRESENTATION OF SOUND..................................... 63 vi  5.1 The mathematical representation of sound ....................................................... 66 5.1.1 The phonetic features for encoding in the mathematical representation of sound ............................................................................................................. 68 5.1.2 Parameters and weights of the mathematical representation of sound .... 70 5.1.3 Optimizing weights, parameters, and cost of indels based on their ability to identify cognates ................................................................................ 73 5.2 Measure of Pronunciation variation based on the mathematical representation of sound ........................................................................................... 80 5.3 Measure of Pronunciation variation based on the non-categorical representation of vowels ......................................................................................... 85 CHAPTER 6: CONCLUSION ..................................................................................... 89 6.1 The limited representation of the Arabic varieties ............................................ 93 6.2 Implications of different local maxima ............................................................. 93 6.3 Computational limitations ................................................................................. 95 6.4 Patterns of sound change and the mathematical representation of sound ......... 96 6.5 Suggestions for future research ......................................................................... 98 REFERENCES ............................................................................................................. 100 APPENDIX A: THE SWADESH LIST FOR THE VARIETIES OF ARABIC UNDER CONSIDERATION ....................................................................................... 105 APPENDIX B: ENCODING THE MATHEMATICAL REPRESENTATION OF SOUND ......................................................................................................................... 177 vii  CHAPTER 1 INTRODUCTION AND OVERVIEW Measures of linguistic variation, also called linguistic distance, is one of the prominent topics in the growing field of dialectometry, which is concerned with quantifying linguistic differences and similarities and, often, relates it to geographical distances between the areas where the relevant languages/varieties are spoken (Nerbonne and Kretzschmar 2003). In this thesis, I report on a set of computational measures of linguistic variation that quantifies the lexical and pronunciation variation between five Arabic varieties: Modern Standard Arabic MSA, Gulf Arabic GA, Levantine Arabic LA, Egyptian Arabic EA and Moroccan Arabic MA. The drive to computationally study linguistic variation is partly due to the extensive typological literature and the increasing number of corpora from different languages, which makes this type of research possible. Dialectometry has the potential to enrich the debates in a variety of fields such as theoretical linguistics and its focus on microvariation and its extents and limits as well as the related issues it raises about the cognitive aspects of language, in addition to anthropology, sociology and history, among many others. This research provides empirical evidence regarding the amount of linguistic variation between the Arabic varieties under consideration. Hence, it provides an answer for the frequently asked question about the closeness of the local varieties to MSA. Moreover, it provides empirical evidence based on computational techniques for two observations about the linguistic communication between speakers of the local Arabic varieties. The first observation is that MA is more distant to the other local varieties of Arabic considered in this study than the other varieties among themselves; Geographically, MA is also more distant. The second observation is 1 that, in most cases, Egyptian speakers are understood by other varieties better than they understand them. It is important to note that this observation may be due to factors related to exposure to Egyptian media, which is popular in many other countries. Also, it may be due to factors related to the linguistic competence of the speakers on both sides. Of course, the former might have effect on the latter, for example, exposure might result with lexical items to be borrowed from one variety to the other, which become part of the linguistic competence of the speakers of both varieties. This research provides evidence about the amount of linguistic variation between the varieties as they are currently spoken. The questions about the reasons that might affect the amount of variation, such as exposure, are outside the scope of this research. The term linguistic distance has been extensively used in the field of dialectometry to express the amount of linguistic variation between varieties. However, this term is problematic as „distance‟ implies a single measure calculated between two objects. As shown by the use of the term mutual intelligibility, the measure of intelligibility is inherently asymmetric, meaning that speakers of some variety (A) may understand speakers of another variety (B) better than speakers of variety (B) understand speakers of variety (A). In this thesis, I develop variation metrics that are asymmetric. Instead of the term linguistic distance, I am using the terms measure of linguistic variation and linguistic variation metric; they are used interchangeably in this thesis. Séguy was among the first researchers in the field of dialectometry. In his 1973 study, he used a linguistic Atlas that contained variables from five linguistic subsystems or components that represent the languages under consideration. The linguistic subsystems were lexical (represented by 170 variables), pronunciation (67), phonetic/phonological (75), morphological (45), and syntactic (68). For each subsystem or component, Séguy calculated the percentage of disagreements between each neighboring pair of sites for each variable in the five subsystems. 2 Then the linguistic distance is calculated as the average of the distances between the five subsystems (Heeringa 2004). In this research, I study each linguistic subsystem independently when measuring linguistic variation, which in our view is the most efficient, informative and feasible way to measure linguistic variation. For the present purposes, the scope of the investigation is limited to two linguistic subsystems: lexical and pronunciation. Other subsystems, such as morphology, morphosyntax and semantics, are to be studied in the future. It is important to explore each linguistic subsystem independently because the amount of linguistic variation in each subsystem might have different implications. For example, from a Natural Language Processing (NLP) point of view, greater variation in the lexical subsystem indicates more differences in a dictionary to be used in an automatic translation system. A smaller variation in pronunciation might imply that an automatic speech recognition system trained on one dialect is usable, to some extent, for the other dialect. Similarly, morphosyntactic and morphological distance should reflect the amount of adaptations or changes required to make a morphological analyzer or stemmer usable for the other variety. The question of measuring linguistic variation has been approached from different perspectives. Some studies have looked at the distance between languages in an effort to reconstruct the languages family trees (Gray and Jordan 2000; Gray and Atkinson 2003; Serva and Petroni 2008, among others). Others have looked at the distance between closely related languages, or dialects of the same language, in an attempt to identify the subgrouping of those languages or dialects (Elsie 1986; Ebobisse 1989; Babitch and Lebrun 1989; Kessler 1995; Heeringa 2004; Valls et al. 2011, among others). Yet another stream of research has employed measures of linguistic variation in computational tasks such as the automatic identification of 3 cognate words (Kondrak and Sherif 2006; Kondrak 2009). Gooskens (2007) tested the correlation between different measures of linguistic variation and mutual intelligibility between Scandinavian languages to show that the amount of phonetic variation can predict the degree of mutual intelligibility better than the amount of lexical variation. Within the area of Arabic linguistics, the most relevant area of research has been concerned with the problem of dialect identification (Biadsy et al. 2009; Zaidan and Callison-Burch 2012; Elfardy and Diab 2013). The motivation for this study is to enhance our understanding of linguistic variation and thereby enhance our understanding of human language as a whole. This is based on the idea that quantifying the amount of variation between two entities enforces a better understanding of the nature of the entities under consideration. The goals of this study are both conceptual and empirical. Conceptually, I develop a representation of sound that captures phonetic similarity in a mathematically simple and computationally feasible way. This representation of sound is based on phonetic features and articulatory gestures; it is an attempt to computationally represent the sound based on its basic components. It is also equipped with the ability to represent sound categorically and non- categorically, this representation of sound is referred to as the mathematical representation of sound. The second conceptual goal is to provide a non-subjective way to assign weights to phonetic features. The first two conceptual goals are crucial to answer the question of how to computationally measure pronunciation variation. Which in turn, leads to models and techniques that could potentially help solve problems related to similarity in pronunciation raised in various NLP tasks. The third conceptual goal is to introduce the idea of measuring linguistic variation asymmetrically. This is important to solve the puzzle of asymmetric mutual intelligibility. Empirically, I develop a set of techniques to measure the amount of lexical and pronunciation 4 variation between closely related (and possibly mutually intelligible) languages. I incorporate data from four local varieties of Arabic and from MSA to measure the lexical and pronunciation variation among them. I provide a new approach to computationally handle pronunciation variation based on a mathematical representation of sound. I also consider which features should be included in the representation of a given sound, and the salience of each of these features. I measure the amount of linguistic variation between all pairs of Arabic varieties included in this study, which answers the frequently asked question about the closeness – here, in terms of lexical and pronunciation variation – of the local varieties to MSA. It is important to keep in mind that I focus on the amount of variation between MSA speakers and hearers from the local varieties, which reflects the ability of the members of local varieties to comprehend MSA. The other direction of communication is not highlighted in the discussion because it relates to the ability of MSA native speakers to comprehend the local varieties; the existence of MSA native speakers is questionable and if exists their ability to comprehend the local varieties would not be of a high cultural and social importance. The primary guideline in making decisions related to the data analysis and the design of the data collection procedure is to mirror the degree of mutual intelligibility between two speakers when they are first encountered or after a limited exposure. It is important to note that the amount of linguistic variation that we are measuring is not the only factor that affects the degree of mutual intelligibility. Exposure is another factor or perhaps one of the most important factors that facilitates mutual intelligibility. Speakers from different dialects maybe exposed to each other and may develop some familiarity with each other‟s dialects. Even if someone is not exposed to some dialect he/she might be exposed to another dialect that has some features that exist in the first dialect. For example, a speaker of the dialect spoken in Cairo, Egypt, does not 5 have gender agreement in verbs for third person plural verb subjects in his/her EA grammatical system. However, since he/she might be exposed to Standard Arabic, which has that feature, we do not expect to see significant intelligibility problems with respect to third person feminine plural agreement with speakers of some GA dialects which have that grammatical feature. The four local varieties are represented by elicitations of the words of the Swadesh list from two native speakers born and raised in a major city where the variety is spoken. MSA is represented by translations of the words of the Swadesh list from two dictionaries of MSA (see chapter 2). The lexical subsystem is investigated at two levels of representation, the word level, and the phonemic level. At the word level, the amount of linguistic variation is based on whether the words have originated from the same linguistic origin. The phonemic level considers more details by measuring the lexical variation based on the similarity of the IPA transcription of words of the Swadesh list. The pronunciation subsystem is investigated at three levels. The first and most abstract level is the phonemic level. At this level, we measure the amount of pronunciation variation based on the similarity of the IPA transcription of cognate words in the Swadesh list. The second level incorporates the mathematical representation of sound which takes into account the phonetic features and articulatory gestures in measuring pronunciation variation. The third level allows the vowels to be represented non-categorically based on the values of the first and second formant frequencies. MSA is not included in the third level due to the lack of acoustic data. All measures that took into account MSA have situated MA as the farthest to MSA. The lexical measure at the word level resulted with LA as the closest to MSA, followed by GA then EA. The remaining three measures have situated GA as the closest to MSA. Two of them had LA in the second place. As for the variation between the local varieties, the closest to MA is EA 6 followed by GA and LA. The variation metrics did not provide a significant distinction between the closeness of GA to MA and the closeness of LA to MA. All variation metrics showed that GA speakers are closer to LA hearers than EA hearers. The lexical measure at the phonemic level and the first two pronunciation measures showed that EA speakers are closer to LA hearers than GA hearers. On the other hand, the third measure of pronunciation variation showed that LA speakers are closer to GA hearers than EA hearers. See Chapter 6 for more discussion about the closeness of the Arabic varieties to each other. For many studies in dialectometry, the focus is categorizing different dialects into subgroups (Elsie 1986; Babitch and Lebrun 1989; Ebobisse 1989). One shortcoming in this approach is that the focus often drifts to defining dialect boundaries, which is not the focus of the current research. Séguy (1973) introduced the idea of providing a distance matrix that replaced the method of counting the number of isoglosses between dialect sites and ruled out the problem of dialect subgrouping. In this project, I follow Séguy (1973) by providing results in a distance matrix as opposed to providing the results on a map. The distances reported by each metric are best interpreted relative to other results from the same metric, reported in the same table. The rest of this thesis is organized as follows. Chapter 2 discusses the data, the data collection procedure and the preparation of the data for the use of the measures of linguistic variation. Chapter 3 reports on the measure of lexical variation at the word level. Chapter 4 describes measures of lexical and pronunciation variation at the phonemic level. Chapter 5 discusses the mathematical representation of sound and respective methodology used in measuring pronunciation variation. The conclusions, limitations, and implications of this research and future directions are discussed in Chapter 6. 7  CHAPTER 2 PARTICIPANTS, DATA SOURCES, AND DATA COLLECTION PROCEDURE This chapter covers all the steps required to prepare the data for measuring the lexical and pronunciation variation between the varieties of Arabic. The first section discusses the data sources used to elicit the words of the Swadesh list. Each local variety is represented by two male native speakers born and raised in a major city where the variety is spoken. MSA is represented by two modern dictionaries of Arabic. The second section reviews the Swadesh list and discusses its usability for the Arabic varieties where we found that some adaptations are required. For example, some meanings are clarified or restricted by context sentence. The third section touches the issue of allowing the participants to provide more than one translation for the items in the Swadesh list. The data collection procedure and tools developed to facilitate the data collection are discussed in the fourth section. The data segmentation and transcription are discussed in sections five and six respectively. Section seven reports on the algorithm I developed to predict landmarks at which the values of the formant frequencies are sampled. The last section discusses a non-categorical representation for vowels based on the values of the first and second formant frequencies. The remaining chapters in this thesis discuss the procedures and methods to measure the lexical and pronunciation variation between the varieties of Arabic based on the data sets prepared according to the methods described in this chapter. 8 2.1 Participants and MSA Data Sources Each spoken variety is represented by two male native speakers between the ages of 21 and 32. All participants were required to have been born and raised in a major city where that variety is spoken; their parents must also speak the same dialect. For this study, we only consider male speakers in order to eliminate any possible effect of gender in the data. I tried as much as possible to have all participants of similar socio-economic status from the middle class. More information about the participants is provided in Table 2.1. Table 2.1: Summary of the participants Dialect ID City Social status Year of Birth EA EA01 Cairo, Egypt Middle, upper 1983 EA EA02 Cairo, Egypt Middle 1982 GA GA01 Dharan, Saudi Arabia Middle 1982 GA GA02 Manamah, Bahrain Middle 1984 LA LA01 Salt, Jordan Middle 1984 LA LA02 Tripoli, Lebanon Middle, upper 1992 MA MA01 Meknes, Morocco Middle 1982 MA MA02 Rabat, Morocco Middle 1982 MSA is represented by two modern dictionaries, namely Almawrid (Ba„albaki and Ba„albaki 1999) and Elias Modern Dictionary (Elias and Elias 1983). Because MSA is a standardized language, the lexical items from these dictionaries are considered an accurate representation of the language. One complication was that the dictionaries listed some dialectal forms such as ʔɛ:ʃ „what‟ from the Levantine dialect haraʃ „rub‟ from the Egyptian dialect. Therefore, these words were removed from the data set after consulting other modern and classical dictionaries of Standard Arabic (muxtaar ʔassiħaaħ, lisaan ʔalʕarab, and ʔassiħaaħ fi ʔalluʁa). Also, because the lexical items in the Swadesh list sometimes had multiple possible 9 translations, I selected only the translations that matched the context assigned to the items (see Section 2.2). 2.2 Swadesh list The Swadesh list is widely used in linguistic research. The list consists of 207 lexical items that contain different parts of speech including pronouns, nouns, adjectives, verbs, prepositions and others. The Swadesh list is provided in Appendix A including the translations from the Arabic varieties and the original English version. Some adaptations are introduced to the list to make it usable for Arabic varieties and to eliminate, as much as possible, the effect of the other linguistic subsystems on the lexical and pronunciation variation. These adaptations are achieved by introducing the word in a context sentence. To be consistent, all words are given context sentences even if the context is not necessary. The first and most frequent adaptation is to select a single verbal form with the same tense and person, gender, and number agreement for all verbs. This is necessary to ensure that the participants are not providing different inflections or tenses for the verbs. All verbs were elicited in the past tense with third-person masculine singular agreement, which has no prefixal or suffixal inflections and is the conventional form listed in Arabic dictionaries. This eliminated as much as possible the effect of the morphosyntactic subsystem. Likewise, the masculine form was selected for the two instances of the pronoun you (singular and plural) for consistency. In addition, nouns were elicited in an indefinite (unmarked) form and cliticized pronouns were removed. Moreover, word final vowels are not included for verbs, nouns, adjectives, or quantifiers, for which the vowel in most case indicate grammatical inflection rather than lexical information. Other lexical categories such as pronouns, demonstratives, question words, negation particles, prepositions, and conjunctions 10 were elicited with the word final vowel maintained because the word final vowel does represent lexical information for these classes. The second adaptation is to provide a context sentence that disambiguate the meaning of some words in the Swadesh list. Although a context sentence is provided for each item, the disambiguation is necessary in three main situations for certain words. The first situation is syntactic where the translation of the item depends on the syntactic position of the word in the sentence. The negation particle not, appearing as item number 16 of the Swadesh list, can be translated in GA as maa to negate a verb and as muu or miʃ to negate a participial or an adjective.1 In such cases, it is important to provide all participants with a single context to ensure consistency. The second situation is based on lexical semantic factors where the translation of the word is highly dependent on the context. For example, adjectives can be translated differently when they modify different nouns. The adjective wide in English can be used in wide road and wide pants. The translation of wide pants in EA is bantˤaluun waaseʕ. While wide road could be translated as both ʃareʕ waaseʕ and ʃareʕ ʕariidˤ. To avoid the situation where the participants are translating the adjectives with different contexts in mind, an explicit context is provided. Likewise, a preposition may have more than one spatial or temporal meaning. For example, I am at home and I am at the door denote different spatial meanings; the first denotes that the entity referred by the subject of the preposition is inside the home, whereas the latter means that the entity referred by the subject of the preposition is close to the door. The third situation is when a word has more than one meaning. For such cases, the context sentence ensures that all participants are translating the same word sense and avoids ambiguity. Examples from the 1 In some varieties of Gulf Arabic, one can find miʃ in addition to the traditional Gulf negatives maa, muu and mub. miʃ is most likely a borrowing due to contact with varieties of Arabic, such as Egyptian, where miʃ is the typical negative particle. 11 Swadesh list include the verb lie which means to rest on a flat surface or to speak falsely. The noun bark might refer to the covering of a tree or to the sound of a dog. Similarly, fat might refer to the white residue in meat or to an overweight person. I selected the word sense that goes in line with the data provided by earlier studies that have used the Swadesh list. In addition to the adaptations mentioned above, it was necessary to introduce some adaptations to a set of items in the Swadesh list. These adaptations are based on the researcher‟s experience in working with participants from the spoken varieties. The translation of item number 40, that corresponds to wife, was elicited in the construct state form (the so-called Idhaafa). Some participants provided an exclusively formal (MSA) translation for the word when it is not in Idhaafa construction, so the word in Idhaafa is considered more natural. It was also problematic to elicit a translation for item number 46, corresponding to bird. The size of the bird plays distinctive role in the translation of the word, so the context sentence specified the size of the bird. The class of demonstratives (items 7-10) was given as a topic of the sentence, and the participants were asked to utter it while pointing to the intended object. The coordination item and (number 204 in the Swadesh list) was produced by the participants in many different ways, including different ending vowels. In many cases, the same participant provided more than one form. Examples of the pronunciations include: ʔu, wa, wu, wi, and u:. To resolve the issue of extensive optionality, I asked the participants to add an epenthetic glottal stop and pause before and after uttering the item. The context sentence for this item is: Ali ___ Saleh are friends. The direction was given as follows: say the first name then pause. Start the coordination element by starting with an ʔ sound if needed, then pause again after uttering the coordinating element. Then say the second name. The pauses enforced the pronunciation of the item as a word rather than a prefix. This strategy worked very well to eliminate the variations caused by different optional 12 pronunciations of the same item between the native speakers of the spoken varieties. However this caused a problem comparing these different pronunciations to MSA because the selected pronunciation of the same item is not possible in MSA, for which the standard form wa was used. 2.3 Allowing Multiple Translations The words of the Swadesh list were elicited in two passes. In the first pass, participants were asked to translate the words from English to their variety of Arabic. In the second pass, participants were given the words in the other varieties, in addition to the English form. The researcher discussed with the participants the possibility of using one of those words or words with similar linguistic origin in their variety used in the same context. The purpose of the first round is to find the most natural translations that the participants would provide without seeing what other participants have provided. The purpose of the second round is to find any possible optionality where a cognate of a word in one of the other varieties is available in the participant‟s variety with the same meaning. It is worth mentioning that some participants have said, in some cases, that the words they saw in the second pass have reminded them with a more natural translation of the English word2. All words are provided in Appendix A. The words that the participants provided in the first round are tagged with ENG, abbreviation of ENGlish. The words elicited in the second round are tagged with VAR, abbreviation of other VARieties. This requirement complicates the data collection procedure because each participant must be aware of all the words provided by the other participants. If a participant adds a new set of words, then all other participants have to be consulted about the newly added words. To simplify the process I 2 An example is the translation of the word „guts‟ (Swadesh item 86) provided by the speaker GA01. After seeing the translation provided by the other speakers, he said that masˤArIn is a better translation that the word ʔamʕAʔ. 13 anticipated what the participants would potentially provide by informally consulting native speakers from the varieties under consideration and by consulting an online resource.3 If a participant introduced a word that did not exist in the list of possible words, the new word was added to a list that I used to reinterview all previously recorded participants. 2.4 Data collection procedure and tools The data collection procedure is designed to allow the participants to provide translations of the English word along with a context sentence before they are shown the words in the Arabic varieties. It is also possible to add to the list of words in the Arabic varieties as I elicit data from the participants. For each item in the Swadesh list, the participant was first given the word in English along with the context sentence. Then the researcher discussed possible translations in his variety. The participants were always reminded that they must only provide words that they produce in informal settings such as when talking to siblings and close friends from the same city. When the participant is ready, he was asked to repeat each translation that fits the context sentence three times. After that, the participant was given a set of possible translations in the Arabic varieties according to the preliminary data I collected about the other varieties and according to what the previous participants have provided. If a cognate of any of the words exists in his variety and would be used in daily life for the given context then the participant was asked to repeat each of these new possible words three times. In many cases, the participants would say that they understand the word and they might have heard it spoken by speakers from their city, but they do not feel that they would say it themselves. In such cases, the word was not considered. In the event the participant added a new word that did not exist in the precompiled 3 http://en.wiktionary.org/wiki/Appendix:Arabic_Swadesh_list 14 list of possible words in the Arabic varieties then the new word is added to the list so the following participants are asked about it. Here it is important to keep track of any added items. After I recorded the data from all participants, I recompiled the list with including the newly introduced lexical items and ask each participant about the items other participants have introduced after their first session. The data collection was facilitated by BrowseHTMList, an application I developed to help manage the process of data collection. For phonetic analysis and manipulation of audio files, the Praat software was used (Boersma and Weenink 2012). This tool is an open source program used by the linguistic research community. The software was also used for the analysis of the recorded stimuli as discussed later in this chapter. The recordings took place in a sound-proof booth at the Phonetics and Phonology Lab at the University of Illinois at Urbana-Champaign using a Marantz digital recorder (Marantz PMD570) and an AKG c520 head-worn condenser microphone. The recordings were sampled at 48.0 kHz. 2.4.1 BrowseHTMList application BrowseHTMList is an application developed by the researcher using MS Visual C++ 2005. Its main function is to load a list of HTML pages, each page is associated with an ID. The application allows a user to browse through the HTML pages in the order they are included in the list. An additional function of the application is to track browsing history times. This is done by starting a timer at the beginning of each session, and the application logs the starting and ending time for viewing each page relative to the timer that was started at the beginning of the session. The accuracy the timer is in the range of ±16 milliseconds according to Microsoft MSDN™. The time log is used later to automatically segment the recording. Figure 2.1 shows a 15 sample of the list given to BrowseHTMList. Figure 2.2 shows the list produced by the program as output that contains time stamps. BrowseHTMList application is designed and developed to be as generic as possible to benefit the research community running similar data collection sessions. To achieve this goal, it is made open source under a GNU license agreement4. Also, it is designed to be easy to customize. The most customizable feature is its ability to host HTML files. HTML provides extensive formatting that is application independent: the font can be changed, pictures can be added, tables can be inserted, and much more. Figure 2.3 shows a snapshot of the application with illustrations about the controls in the application. Figure 2.1 Part of the list provided as input to the BrowseHTMList application. The first column contains Swadesh list item ids, and the second column contains the HTML page file names no_content_intro_SWADESH_012 SWADESH_012_01_intro.html SWADESH_012_eng_utter_01 SWADESH_012_02_utter_1.html SWADESH_012_eng_utter_02 SWADESH_012_03_utter_2.html SWADESH_012_eng_utter_03 SWADESH_012_04_utter_3.html no_content_other_var_SWADESH_012 SWADESH_012_05_other_varieties.html SWADESH_012_var_utter_01 SWADESH_012_02_utter_1.html SWADESH_012_var_utter_02 SWADESH_012_03_utter_2.html SWADESH_012_var_utter_03 SWADESH_012_04_utter_3.html Figure 2.2 Part of the list produced as output from the BrowseHTMList application. The first column contains Swadesh list item ids, the second and third columns contain the timestamps in milliseconds of browsing the page relative to the time of loading the list no_content_intro_SWADESH_012 998.406000 1006.986000 SWADESH_012_eng_utter_01 1006.986000 1008.780000 SWADESH_012_eng_utter_02 1008.780000 1011.027000 SWADESH_012_eng_utter_03 1011.027000 1013.289000 no_content_other_var_SWADESH_012 1013.289000 1056.423000 SWADESH_012_var_utter_01 1056.423000 1059.231000 SWADESH_012_var_utter_02 1059.231000 1064.410000 SWADESH_012_var_utter_01 1064.410000 1069.090000 4 BrowseHTMList is downloadable at: https://browsehtmlist.codeplex.com/ 16  Figure 2.3 Snapshot of BrowseHTMList application when the participant is browsing the first item of the Swadesh list. The menu bar shows controls to move to the next page and to move the previous page. The status bar shows the number of items the participant is expected to browse. The content page shows the word in English, the context sentence, and a request to the participant to be prepared to record the translated word in the following pages A data collection session facilitated by BrowseHTMList would start by starting the sound recording device then loading the list of HTML pages to be browsed during the session. This list should be saved in a text file in the same folder where the HTML pages are located. The “File  Open” menu item loads the list and instantiates a timer that will be used to track the time, loads the first page in the list, and specifies the number of pages that will be browsed in the session in the status bar of the application along with a sequence number of the currently browsed page. 17 After loading the list, I play a beep by the “Item  SyncBeep” menu item. This beep is used to segment the WAV file as discussed in section 2.4.2. Then the participant or the researcher browse through the list by clicking on the menu items “Next” and “Previous”. It was found that the mouse clicks generate undesirable noise in the WAV signal. To avoid this noise, I added the functionality of accessing the menu items using keyboard shortcuts where the keyboard was found to generate less noise. The keyboard shortcuts are “Ctrl+N” for “Next”, and “Ctrl+P” for “Previous”. To add more flexibility, I added three more menu items in addition the “SyncBeep” menu item under the “Item” menu item to allow the user to skip an item, redo an item, or mark an item as a bad item. These are accessed through “Item  Skip” or “Ctrl+S”, “Item  Retry” or “Ctrl+R”, and “Item  MarkBad” or “Ctrl+B”. Skipping an item is useful in cases where the participant is not recording anything related to the HTML page he/she is viewing. In case of a mistake or mispronunciation, the participant can mark the current item as illformed by clicking on the MarkBad menu item, which shows an indicator on the status bar of the application. Then, redo the recording of that item. There is 0.5 second delay added to the transition between HTML pages when the user moves to the next or previous pages. This delay is to enforce a pause by the participants especially in cases when they are asked to repeat the same word three times because they must wait for the next page to load before each utterance. The data collection session ends by playing another beep then stopping the recording device. The application generates a log file about the browsing session and saves it in the same folder that has HTML pages. See Figure 2.2 for a sample of the log file. 18 2.4.2 Synchronizing the timestamps and TextGrid boundaries The lab setting consists of two main systems. The first system records the participant‟s voice. This system involves an acoustic controlled environment, a microphone and a sound recording device, to be referred to as the recording system. The second system is used to control the flow of the data collection. It presents instructions and stimulus items to the participant, and keeps track of the time it takes in each stimulus item, to be referred to as the flow system. The second system is facilitated by the BrowseHTMList application. The recording system generates audio recordings in a WAV file while the flow system generates Swadesh list item IDs and timestamps. Linking the timestamps provided by BrowseHTMList within the flow system to the WAV signal of the recording system requires synchronizing the two systems. The synchronization is achieved by having one system generating a signal that is detected by the other system at exactly the same moment. The signal is a sound generated by the flow system and recorded by the recording system. The sound signal is designed to be easy to detect in the WAV signal. It is a beep that the flow system plays within the BrowseHTMList application, this beep is referred as SyncBeep.5 As mentioned earlier, a menu item is added to the BrowseHTMList application to play the SyncBeep at the beginning of the recording session. Then a Praat script is used to synchronize the timestamps provided by the flow system with the WAV signal. The synchronization provided a perfectly aligned TextGrid in cases where the recording session was short. In long recording sessions where the flow system was operated by the personal laptop of the researcher6, there was a consistent trend where the beginning of the TextGrid is perfectly aligned while later interval boundaries were more shifted. This consistent pattern seems to be due to inaccuracy of the time tracking hardware in that 5 The SyncBeep is created using http://www.linguistics.ucla.edu/faciliti/facilities/acoustic/create_waveforms.txt [Type – square, duration – 1 sec, sampling rate 16000, F0 450, amplitude 1, triangle skewness 50] 6 The researcher‟s laptop is Dell© Vostro 3500. 19 particular machine. The shift did not appear when the flow system was operated on another machine. To resolve the problem of misaligned TextGrids, I added the capability of the flow system to generate two SyncBeeps. The first SyncBeep was played at the beginning of the recording session and the second SyncBeep was played towards the end of the recording session. The following equations show the method to calculate the timestamps to set the boundaries of the TextGrid segments based on the time the two SyncBeeps generated by the flow system and detected by the recording system. x: the input timestamp as indicated by the log generated by the BrowseHTMList application. b: the difference between the time logged for the first SyncBeep and the time the SyncBeep is actually located in the WAV file a: the time multiplication factor calculated based on the first and second SyncBeeps y: the output time to be used to mark the TextGrid boundaries. y = ax + b 2.5 Data segmentation Each data collection session resulted with a sound recording and a log file containing item IDs and timestamps generated by BrowseHTMList. The timestamps were synchronized using the procedure described above. After that, a TextGrid was generated where each line in the log file was associated with its relevant part of the WAV signal. To improve the quality of the alignment, I ran a Praat script that marks the pauses in the WAV signal in a TextGrid.7 There are 7 This step is achieved with the help of mark_pauses.praat. A script developed by Mietta Lennes, available at: http://www.helsinki.fi/~lennes/praat-scripts/public/mark_pauses.praat 20 two TextGrids at this stage, the first marks the IDs of the Swadesh items in the TextGrid intervals. The second marks the intervals of pauses and speech in another TextGrid. A script is developed to get the utterance IDs of each Swadesh item from the first TextGrid, and attach it to the best matching utterance interval in the second grid. This generates a new TextGrid with Swadesh items marked in the intervals of speech. To review the results, all the TextGrids are combined and a script goes though the potential lexical items and plays them for the researcher to review. Mistakes can be easily fixed by having all TextGrids in one Praat window. A manual review was necessary to check for errors and fix cases where the boundaries were not set correctly or when words are given an incorrect ID. Figure 2.4 shows a sample of a TextGrid. At the end of this stage, I ran a script to resolve any possible redundancy in interval IDs, then another script to segment the WAV signal based on the TextGrid. Each interval ID was given to the extracted file name. The last step is to allow simpler accessibility by creating an HTML page where all items are listed along with their IPA transcription, Arabic script, and links to the sound files of the three utterances. Figure 2.4 Snapshot from a TextGrid for the production of Swadesh item number 3 as produced by one of the participants. Starting from bottom up: Tier 3 contains the results of the mark pauses step. Tear 2 is the result of the TextGrid generated based on BrowseHTMList. Tear 1 is the TextGrid used to segment the WAV signal 21 2.6 Transcription All words were transcribed in both Arabic script and IPA. Arabic script is used to build the list of words that the participants will be given as words in other varieties. All participants were born and raised in a major city in the Arab world so they are expected to be capable of reading words in Arabic script. IPA script is later used as input to the measures of variation (see Chapters 3-5). To minimize the amount of manual work, I developed a script to generate an IPA transcription based on the Arabic script. Avoiding complications in the process of generating IPA script based on Arabic script was achieved by following a set of guidelines to transcribe in Arabic script. The primary guideline of the transcription based on Arabic script is to have one-to-many mapping between the Arabic symbols and the IPA symbols. The first guideline causes alterations to way the script is normally used. So, the secondary guideline to have the resulting Arabic transcription as comprehendible as possible to educated native speakers of Arabic. I used only one symbol to represent a glottal stop in Arabic script, the symbol is (‫ )ء‬which is one of the symbols representing the glottal stop. I also borrowed letters from Persian script to represent some sounds that are not represented in Arabic script, the introduced symbols are ‫ گ‬and ‫ چ‬to represent /g/ and/ tʃ/ respectively. In addition, I used the diacritic sukuun (used to mark the absence of a vowel in standard Arabic script) to the mark short mid vowel /ə/. All long vowels are transcribed as ‫ا‬, ‫ و‬and ‫ي‬. Then, each letter in Arabic is mapped to the corresponding IPA symbol according to Table 2.2. ‫ و‬and ‫ ي‬are considered glides if they are preceded or followed by a vowel and transcribed as w and y respectively. Otherwise, they are transcribed as long vowels U and I respectively. Some sounds vary from one variety to another; these are mainly ‫ ج‬and the word final taa marbuuta ‫ة‬. They are mapped depending on the participant‟s dialect. Note that the 22 Egyptian variety does not have the sounds ʒ and ʤ so the standard Arabic ‫ ج‬is used to represent the sound g, this increases the comprehensibility of the resulting script. After the auto conversion from Arabic to IPA, all words were manually reviewed and corrected in cases of mistakes. As mentioned earlier, Appendix A contains all the words of the Swadesh list form all participants. MSA‟s vocalic system contains three vowel categories. Each of the three categories consists of short and long vowels. Short vowels are represented by diacritics and long vowels are represented by letters in the orthography of the language. The number of vowel categories in the spoken varieties is larger. Quantifying the variation between varieties of different vocalic systems is problematic because the variation depends on the granularity of defining vowel categories. Providing a fine-grained representation of vowels that captures, for example, the contrasts between vowels due to the presence or absence of emphatic consonants leads to having a large amount of variation that is derived from having different vowels; however the vowels might be close phonetically. To resolve this problem, I provide two measures of pronunciation variation. One based on a small number of vocalic categories and the other based on the formant frequencies of the vowels in each utterance. MSA is excluded from the measure of pronunciation variation based on the formant frequencies because of the lack of the acoustic data. At the other level where the variation is based on a small number of vowels, the representation of vowels in the spoken varieties should be made comparable to what we know about MSA‟s vocalic system. One might think that it is better to limit the number of vowel categories to three in an effort to have the same number of vowels in the spoken varieties and in MSA. However, the existence of mid vowels, including schwa, in the dialects makes the problem more complicated. One approach to solve this problem is to set the number of vowels to four in the dialects. So, we have three vowels that are considered similar to the vowels in MSA and a mid-vowel. This is not to 23 claim that the spoken varieties have only four vowels in their vocalic inventories; most of them have more. The use of four vowels only is justified because we are comparing against MSA and because we have a more fine-grained representation of vowels based on the first and second formant frequencies at a level of comparison where MSA is not included. The conversion of the consonants in MSA from orthographic letters to IPA is based on the researcher‟s knowledge of the language and based on the available references of Arabic. The pronunciation of ji:m (‫ )ج‬is considered to be a voiced alveolar affricate ʤ (Holes 2004, p. 58). Table 2.2: Mappings of Arabic letters to IPA letters Arabic Script IPA Arabic Script IPA Arabic Script IPA ‫ء‬ ʔ ‫ض‬ dˤ ə For LA ‫ة‬ ‫ب‬ b ‫ط‬ tˤ Variety a Otherwise ‫ت‬ t ‫ظ‬ ðˤ dependent ʤ For MSA and GA conversions ‫ث‬ θ ‫ع‬ ʕ ‫ج‬ʒ For MA and LA ‫ح‬ ħ ‫غ‬ ʁ g For EA ‫خ‬ x ‫ف‬ f ‫ا‬A ‫د‬ d ‫ق‬ q w If preceded or followed by a vowel Long ‫و‬ ‫ذ‬ ð ‫ك‬ k vowels U Otherwise (long back high vowel) and glides ‫ر‬ r ‫ل‬ l y If preceded or followed by a vowel ‫ي‬ ‫ز‬ z ‫م‬ m I Otherwise (long front high vowel) ‫س‬ s ‫ن‬ n َa low vowel ‫ش‬ ʃ ‫ه‬ h Short َu back high vowel ‫ص‬ sˤ ‫گ‬ g vowels َi front high vowel َə ‫ چ‬¢affricate. Voiceless postalveolar mid vowel Also written as / tʃ/ َ+ germination for consonants 24 2.7 Predicting vowel landmarks Measuring the formant frequencies for vowels starts by locating a landmark where the formant frequencies are to be sampled. I developed an algorithm to predict a landmark for vowels in the acoustic signal using the IPA transcription as one of the parameters. Vowels are expected to be in the syllable nucleus position and they are expected to be the loudest phonetic segments in the acoustic signal. Mermelstein (1975) developed an algorithm to segment the acoustic signal into syllables based on loudness maxima and minima. The loudness, as he defined it, is a time smoothed and frequency weighted summation of the energy content. De Jong and Wempe (2009) developed an algorithm to detect syllable nuclei in an effort to measure speech rate. Their algorithm is based on locating intensity peaks that are preceded and followed by dips in intensity. Following the same principle, the developed algorithm locates the vowels based on loudness. The inputs to the algorithm are the acoustic signal, the IPA transcription of the word, and the approximated average formant frequencies of the vowels for the speaker. The output is a list of vowels that existed in the input IPA transcription and the predicted landmark for each. A value less than zero is assigned to the landmark when the algorithm fails to locate the vowel. The availability of the IPA transcription is an additional clue that Mermelstein (1975) and De Jong and Wempe (2009) did not have. There are three main benefits of having this extra input: the exact number of vowels, and therefore the number of loudness peaks, is known; the vowels and the approximate values of the formant frequencies are also known – this is given as input to the algorithm; and the number of voiced segments is known, so we can map each vowel to its corresponding voiced segment. The process of predicting vowel location is divided into four stages using heuristics to automatically locate vowel landmarks with significantly higher than chance accuracy (see Table 25 2.5). The first stage limits the range of the location prediction by mapping the relevant voiced segment in the IPA transcription to the corresponding voiced segment in the acoustic signal. Identifying voiced segments in the acoustic signal starts by calculating the value of F0 every 1ms, which is a shorter interval than the shortest possible pulse period. Consecutive values of F0 that are within the range of valid values for pitch are considered voiced segments.8 Supplementing this, consecutive voiced phones in the IPA transcription are also considered voiced segments. If the number of voiced segments in the IPA transcription equals the number of voiced segments in the acoustic signal then the location of voiced segments of the IPA transcription are mapped one-to-one to the corresponding voiced segments of the acoustic signal. If the number of voiced segments in the acoustic signal is more than the number of voiced segments in the IPA transcription then I assume that there are some voiced segments in the acoustic signal that are divided into more than one segment. This issue is resolved by repeatedly merging the smallest voiced segment in the acoustic signal to the closest voiced segment to it until we reach an equal number of voiced segments. Figure 2.5 shows an example of merging two voiced acoustic segments. This problem is apparently due to having some parts of the voiced segment where the calculation of the pitch did not provide a valid value. Which in turn, could be due to creakiness or some distortion in the acoustic signal. 8 This task is accomplished by running the Praat command: To Formant (burg)… 0 5 5000 0.025 50. The threshold for pitch is set to 170 Hz, values more than the threshold are not considered valid pith values. Calculating the pitch using other techniques or having another dataset might result with another threshold. 26  Figure 2.5 The utterance “baʕdˤ” provided by EA01 in translation for Swadesh item number 20. The utterance is articulated as two voiced segments, the two voiced segments are merged When there are more IPA voiced segments than acoustic voiced segments, the solution is somewhat more complicated. This could happen if some of the voiced segments were devoiced in some contexts or if some of the unvoiced segments were voiced in a context of voiced segments. The solution is to predict the devoiced segments and ignore them from the IPA transcription. If the mismatch in the number still exists, I force merge the voiced segments in both sides. The force merge accounts for cases of voicing a voiceless phoneme in context of voiced phones. After closely considering the dataset in hand, I composed four phonological rules to account for the cases of devoicing of phonetic segments at word boundary positions. This strategy resolved most of the problems in the dataset. Nevertheless, different languages or datasets might have different rules or different ordering of rules. The rules in the order used for the Arabic dataset in this study are as follows: 27 1. Ignore a vowel between two voiceless stops in the word initial position. This rule accounts for devoiced vowel between two voiceless stops in word initial position. In such cases, vowels are predicted to be voiceless. Figure 2.6 shows an utterance where a schwa between two voiceless stops was devoiced in word initial position. This caused the acoustic signal to have only one voiced segment while the IPA transcription indicated two. The algorithm ignores the first voiced segment in the IPA transcription and matches the remaining voiced IPA segments to the corresponding segments in the acoustic signal. 2. Ignore a voiced consonant or vowel after a voiceless consonant in the word final position. This rule accounts for devoiced segments of single phoneme after a voiceless consonant in word final position. Figure 2.7 shows an utterance where a word final nasal was devoiced, or barely detectable, after a voiceless stop. The last voiced segment in the IPA transcription was ignored. 3. Ignore a voiced word initial consonant before a voiceless consonant. This rule accounts for a devoiced consonant cluster containing a voiced consonant followed by a voiceless consonant in word initial position. Figure 2.8 shows an utterance where the word initial voiced stop was devoiced before a voiceless fricative. The first voiced segment in the IPA transcription was ignored. 4. Ignore a vowel following a voiceless stop and preceding a voiceless consonant in word initial position. This rule accounts for devoiced vowels after a word initial voiceless stop and before a voiceless consonant. In such case, the vowel is predicted to be voiceless. Figure 2.9 shows an utterance where a vowel after a voiceless stop and before a voiceless fricative was devoiced. Similar to the first three rules, one of the voiced IPA segments is ignored. In this rule, the ignored voiced IPA segment is the first one. 28  If the number still does not match then merge the voiced IPA segments starting by the first voiced segment and ending by the last voiced segment into one segment. Similarly, merge the corresponding voiced acoustic segments. This ensures that the number of voiced segments in both the acoustic signal and the IPA transcription are equal to one. Therefore, the algorithm never fails to match the number of segments. Figure 2.10 shows an utterance where a voiceless fricative was detected as voiced fricative in context of voiced phones. This is a frequent phonological change that is recovered by the merger rule. Figure 2.6 The utterance “kətIr” provided by EA01 in translation for Swadesh item number 18. The Schwa is devoiced 29 Figure 2.7 The utterance “batˤn” provided by EA01 in translation for Swadesh item number 85. The utterance final nasal is devoiced Figure 2.8 The utterance “gsˤIr” provided by GA01 in translation for Swadesh item number 33. The utterance initial voiced stop is devoiced 30 Figure 2.9 The utterance “ʔaħmar” provided by GA02 in translation for Swadesh item number 172. The first vowel is devoiced Figure 2.10 The utterance “stafraʁ” provided by EA01 in translation for Swadesh item number 20. The voiceless fricative /f/ is voiced in context of voiced phonemes 31  The second stage of the algorithm is to evaluate the loudness of the acoustic signal. The first step is to split each voiced segment of the acoustic signal into acoustic units where the loudness is computed and compared, the acoustic units are the pitch periods identified in range of 75 to 500 9. For each pitch period in the acoustic signal, the loudness is calculated based on two methods. The first method calculates loudness based on the average amplitude of the absolute values of the sound pressure values, to be referred as the average amplitude method. The second method calculates loudness based on the maximum value of sound pressure minus the minimum value of sound pressure in the pitch period, to be referred as the max-min method. Each method generates a sequence of values representing the loudness of the acoustic signal of the relevant voiced segment. Both of these sequences of values are considered later in the analysis. Figure 2.11 shows the pitch periods and the results of two methods of evaluating the loudness in an utterance containing two voiced segments; the first voiced segment contains two vowels and one vowel in the second voiced segment. In the third stage, I locate a preliminary landmark for the vowels based on the maxima in the loudness sequences. If the number of maxima in the loudness sequence equals the number of vowels in the corresponding voiced segment, then the location of maxima are set as preliminary predicted vowel landmarks. However, due to natural fluctuations in the acoustic signal, the number of maxima is often more than the number of vowels. In that case, the loudness sequence is smoothed repeatedly with the Simple Moving Average (SMA) algorithm (window size 3) until the number of maxima is equal to or less than the number of vowels. SMA recalculates the value of each point in the sequence as the average of the point itself and points before and after it. So, each value at index i is calculated as the average of the values at indices i-1, i, and i+1. In the 9 This task is accomplished by running the Praat command: To PointProcess (periodic, cc)... 75, 500 32 event that the state of equal number of maxima and vowels was not reached, the prediction based on a loudness sequence fails. For the purposes of this study, SMA provided satisfactory results. However, the vowel prediction algorithm can be potentially improved by experimenting with different smoothing techniques. Figure 2.12 shows the repeated smoothing of the loudness sequences of the utterance plotted in Figure 2.11. For instance, the loudness sequence of the first voiced segment evaluated using the max-min method contained three maxima while the relevant voiced IPA segment contained two vowels. After smoothing the sequence one time, there are still three maxima. Smoothing the sequence again resulted with two maxima that are used as preliminary predicted landmarks for their corresponding vowels. As mentioned earlier, the preliminary predicted vowel landmarks are set to the maxima of the repeatedly smoothed loudness sequences once a state with an equal number of maxima and vowels is reached. If the preliminary predicted vowel location happened to be in a pulse where the value of the first formant frequency or the value of the second formant frequency is not stable then the algorithm scans for the closest stable pulse located between the minima around the maximum of the preliminary predicted vowel landmark. The stability of a pitch period regarding the formant frequencies is defined by having a standard deviation of the values for both the first and second formant frequencies in the pitch period of less than 5010. If a stable pitch period is found then the predicted vowel landmark is set to the center of the closest stable pitch period; otherwise the predicted vowel landmark is set to the preliminary vowel landmark. By the end of this stage, we have predictions from two methods for each vowel with the possibility of failure in one of them or both. Each prediction is evaluated based on the two definitions of loudness in the previous stage and based on the stability of the first and second formant frequencies. 10 The value of 50 is an ad-hoc number that was set based on trial and error. 33 Figure 2.11 The utterance “samaka” provided by EA01 in translation for Swadesh item number 45. With illustration of the two methods of evaluating the loudness Figure 2.12 The utterance “samaka” provided by EA01 in translation for Swadesh item number 45. With illustration of the prediction of the preliminary vowel landmarks using the two methods 34  The fourth stage compares the two preliminary prediction values and selects the one that generated values of the first and second formant frequencies closest to the expected values for the first and second formant frequencies of the vowels encoded in the IPA transcription. This stage requires the average values of formant frequencies for each speaker and for each vowel to be estimated. The estimation of the formant frequency values for each speaker and vowel is based on a manually segmented sample of the data. The sample of vowels is a subset of the elicitations of the Swadesh list. From each participant, three distinct words containing productions of each of the four vowels were selected. The total number of the manually segmented vowels is 288 (8 participants × 4 vowels × 3 words × 3 repetitions for each word). For each vowel production, I measured the first and second formant frequencies at the mid-point of the vowel. Then I deleted one of the three repetitions that generated the most distant formant frequencies resulting in 192 vowels. Then I manually deleted 44 vowels because they provided outlying, unreliable values for the formant frequencies. After the last step, the average was calculated based on the remaining 148 utterances. So each vowel‟s formant frequencies are based on 3 to 6 utterances. Table 2.3 shows the values of the formant frequencies for the vowels for each participant. The estimated values of the formant frequencies were used to select one of the two vowel landmarks predicted by the previous stage of the algorithm. Table 2.4 shows the number of vowels in the data set, the number of predictions produced by each method and the number of predictions selected from each method, as well as the number of vowels for which both methods failed. To test the accuracy of the prediction algorithm, I randomly selected 132 words from the dataset. All word repetitions are manually segmented so the start and end points for each vowel are known. 737 vowels existed in this data set. The algorithm described above correctly detected 35 650 vowels of the testing data set: 88.2% of the vowels are correctly detected. The 11.8% failed cases are either due to a failure to smooth the loudness sequence so that the number of maxima equals the number of vowels or due to a misprediction where the predicted vowel landmark is outside the vowel. Table 2.5 summarizes the results of the testing data set. Given the large number of vowels in the study where a manual segmentation of the vowels is not feasible, the accomplished accuracy is considered satisfactory. Table 2.3: The values of the manually calculated vowel formant frequencies for all vowels for each participant SPEAKER_ID VOWEL formant1 formant2 a 450 1533 EA01 ə 370 1621 i 242 2299 u 297 840 a 489 1481 ə 333 1622 EA02 i 272 2083 u 325 843 a 450 1170 ə 426 1224 GA01 i 340 1974 u 340 1006 a 612 1219 ə 454 1331 GA02 i 315 2169 u 412 766 a 510 1326 ə 384 1481 LA01 i 293 2270 u 340 864 a 370 1313 ə 297 1184 LA02 i 273 2018 u 328 739 36 Table 2.3: (cont.) The values of the manually calculated vowel formant frequencies for all vowels for each participant SPEAKER_ID VOWEL formant1 formant2 a 603 1347 ə 473 1413 MA01 i 302 2154 u 450 988 a 463 1319 ə 374 1312 MA02 i 262 2348 u 383 938 Table 2.4: Results of the prediction algorithm Count Percentage Vowel count 9534 100 Cases of vowels predicted to be voiceless by phonological rules 27 0.28 Predictions produced by the average amplitude method 9200 96.5 Predictions produced by the max - min method 9224 96.75 Selected predictions produced by the average amplitude method 6664 69.9 Selected predictions produced by the max - min method 2723 28.56 Both methods failed to predict a landmark 120 1.26 Table 2.5: Results of testing the prediction algorithm Count Percentage Vowel count in the testing data set 737 100 Cases of vowels predicted to be voiceless 1 0.14 Vowel predicted correctly 650 88.2 Vowel predicted incorrectly 75 10.18 Failed to predict a vowel landmark 11 1.49 37 2.8 The non-categorical representation of vowels I obtain a non-categorical representation of vowels by representing each vowel by two numbers derived from the values of first and second formant frequency at the predicted landmark. The objectives of this approach are to provide a more fine-grained representation of the vowels than the one provided by the four categories used in the transcription and to rule out the potential subjectivity of the researcher in deciding what the vowel is in each word. The guidelines of the design of the proposed representation of vowels are (1) to have each vowel represented by two numbers that reflect the place of articulation and the degree of constriction at the place of articulation. (2) To have a considerable amount of the values between 0 and 1. This guideline is used to simplify the way the values are used to calculate the pronunciation variation (chapter 5). The last guideline is (3) to rule out physiological differences among the vocal tracts of the speakers. I follow an eight-step procedure to achieve the proposed representation of the vowels. The first step is to calculate the values of the first and second formant frequencies at the predicted landmarks for the three repetitions of each Swadesh list item. Therefore, each vowel in each word is represented by three estimations of the formant frequencies. The second step is to delete the prediction that generates the most distant formant frequencies from the average formant frequencies; the averages were calculated for each speaker and for each vowel based on a sample as described in section 2.7. The third step is to eliminate outliers. For each vowel category and for each speaker, I calculated the mean and standard deviation of the distance between the formant frequencies of the vowels and the average formant frequencies. Vowels that are distant more than four times the standard deviation of the distances between vowels and the relevant average vowels were set as outliers. The fourth step is to recalculate the average formant 38 frequencies based on the results of steps one through three and to repeat step two and step three based on the newly calculated averages. This step is motivated because we now have a data set that enables us to achieve a more accurate averages, the averages used in the previous step were based on a relatively small sample as described in Section 2.7. Table 2.6 shows the values of the recalculated averages of the formant frequencies. The fifth step is to assign default values for the formant frequencies of the outliers, the cases of failed predictions and the cases of predicted voiceless vowels as reported in Table 2.4. The default values are the average formant frequencies for each vowel for each speaker. The sixth step is to normalize the values of the formant frequencies to eliminate the physiological differences among speakers. I used Nearey (1978) normalization technique using “Nearey1, formant intrinsic” technique as implemented by Thomas and Kendall (2007)11. The seventh step is to scale the normalized values so that a considerable amount of the values is in a range between 0 and 1. This is done by calculating the overall average for the first and second formant frequencies for each vowel across speakers, keep in mind that the previous step normalized the differences between speakers. The smallest and largest averages are scaled to 0 and 1 respectively. The same ratio of scaling applies to all vowels. This method of scaling resulted in 48% of the scaled formant frequencies being in the range of 0 and 1 for both scaled formants. Figure 2.13 shows the positions of the scaled values of the formant frequencies for the vowels. In addition, it shows the dispersion of the values by circles marking one standard deviation around the average values, dashed circles correspond to short vowels and solid circles correspond to long vowels. The corners of the dotted box represent the values ((0,0), (0,1), (1,1), (1,0)). Note that each edge lies on at least one of the averages of either F1 or F2. The eighth step is to encode the vowels in the IPA transcription of the words in the Swadesh list non-categorically based on the values calculated in the seventh step. 11 The calculation is based on http://ncslaap.lib.ncsu.edu/tools/norm/norm1.php 39 Table 2.6: The values of the manually calculated vowel formant frequencies for all vowels for each participant SPEAKER_ID VOWEL formant1 formant2 a 482 1283 EA01 ə 389 1101 i 317 2037 u 340 801 a 451 1391 ə 373 1638 EA02 i 256 2227 u 308 923 a 525 1338 ə 508 1229 GA01 i 340 1926 u 370 1047 a 604 1396 ə 563 1437 GA02 i 308 2225 u 382 777 a 534 1272 ə 360 1542 LA01 i 290 2270 u 327 956 a 478 1383 ə 279 1554 LA02 i 317 1898 u 297 909 a 492 1319 ə 460 1338 MA01 i 313 1898 u 380 1010 a 431 1331 ə 446 1377 MA02 i 308 2047 u 349 1041 40 Figure 2.13 Plot of vowels produced by the speakers. The circles show one standard deviation around the average formant frequencies for each vowel. Dashed circles correspond to short vowels. The corners of the dotted box represent the values ((0,0), (0,1), (1,1), (1,0)) 41  CHAPTER 3 MEASURE OF LEXICAL VARIATION BASED ON THE PERCENTAGE OF NON- COGNATE WORDS In this chapter, I report on a variation metric based on the percentage of non-cognate words in the Swadesh list. The basic assumption is that the closer the varieties are to each other the more likely they are to have cognate words with the same meaning. A pair of words is identified as cognates if they share the same linguistic origin. Cadora (1979) used a similar method to assess the lexical relationships among major Urban Syro-Lebanese varieties. He used a list of 200 items that consisted of 100 items from the Swadesh list and 100 items from Ferguson-Sa‟id‟s list12. Cadora highlighted the possibility of having a pair of cognate words in two varieties with different meanings or with slightly different meanings. He gave the example of the meaning of „bed‟ in Damascus and Aleppo as taxit and sariir respectively. A cognate of sariir exists in the variety of Damascus with the meaning of „crib.‟ This highlights the importance of specifying the context of the words in the Swadesh list. An example from the Swadesh list used in the current research is the word fat that has two senses, as a noun it means the substance fat found in human and animal bodies and as an adjective it means obese. The first sense can be translated as simiin in EA, according to the informant we had. While a cognate of the Egyptian word, smiin in LA means the second sense of the word, obese. Presenting the participants with only the English words might lead to such confusion, where translation of different senses might be provided. To eliminate confusion, Cadora defined the term of contrastive compatibility as a pair of non-cognate words with the same meaning. In the present 12 Ferguson-Sa‟id‟s list is not published as far as I know. 42 study, this problem is resolved by specifying a disambiguating context to each item in the Swadesh list, the context enforces all elicited items to have the same meaning. Cadora employed the same widely accepted method of using the percentage of non- cognate words to measure lexical distance. He found a correlation between the geographical distance and the lexical distance of some varieties. He divided the Syro-Lebanese varieties into three main groups that reflected their geographical locations. The Lebanese varieties, together with the Syrian variety of Latakia, are categorized as the western group. The dialect of Deir- Ezzor stands alone in the eastern group. The other major Syrian varieties – of Damascus, Homs, Hama, and Aleppo – constitute the central group. He also examined other major varieties of Arabic outside the Syro-Lebanese area and the lexical distance between these varieties and all the Syro-Lebanese varieties in his study (Cadora 1979). Kessler (1995) used two methods to define cognates. In the first method, called etymon identity, words are defined as cognates if their stem has the same ultimate derivation. In the second method, called word identity, words are defined as cognates only if the words are also cognates at the morphemic level; each morpheme in the word must be cognate in the pair of words. Kessler compared the two methods against previously developed traditional methods to develop dialect maps. The first method seemed to resemble the traditional methods more than the second. In addition to these two basic methods, Kessler developed other metrics that are reviewed with more details in Chapter 4 and Chapter 5. Gray and Atkinson (2003) used the idea of cognate words to estimate when a set of Indo- European languages diverged from each other. They looked at the shared cognate words between the languages under consideration. It is important to note that the definition of cognate words 43 they used is different from the one we are using. The main difference is that Gray and Atkinson (2003) exclude cases of borrowing from the list of cognates; as for the present study, words are considered cognates if they have the same linguistic origin, whether by borrowing or genetic inheritance. The difference is justified because the purposes are different. The purpose of the current study is to reflect the degree of mutual intelligibility. On the other hand, Gray and Atkinson‟s goals were to estimate the divergence time between the languages. The decision of whether a pair of words are cognates or not is a subjective judgment based on the researchers‟ knowledge of the language. For each entry in the Swadesh list, I assign a unique ID for the set of words that are considered cognates. A table containing decisions of cognates and non-cognates is provided in appendix A. The design of the lexical variation metric between two varieties in this study is based on the likelihood of a word to be produced as a translation of the Swadesh list item by a speaker to express the meaning implied by the context sentence and the likelihood of a cognate of that word to be also produced by a hearer from the other variety. One of the guidelines for the data collection is to have the participants provide only the words that they would produce when they speak their variety. If both the speaker and hearer would produce a word from the same linguistic origin to express the same meaning then intelligibility is expected to be achieved, which should be implied by a smaller amount of variation. For example, item 39 of the Swadesh list, „child‟ in reference to a 5 years old child as the context specifies, is produced as walad and ʕayyil by EA01 (the first Egyptian participant), and is produced as tˤifl and ʕayyil by EA02. Since the Egyptian variety is represented by the two participants in this research, the likelihood of the word ʕayyil to be produced is 50%, and 25% for each of the other two words. From the perspective of a hearer from the Gulf variety, a cognate of tˤifl is available, while no cognate of walad or ʕayyil was 44 provided by the Gulf participants. Therefore, 25% of the possible forms are shared in the Gulf variety; the contribution of item 39 in the Swadesh list to the lexical variation metric for an Egyptian speaker and Gulf hearer is an amount of 0.75. Applying the same procedure for all words in the Swadesh list and taking the average of the contribution of each Swadesh list item yield the measure of lexical variation in Arabic varieties. The current algorithm gives words provided by the speaker(s) equal weights. It might be considered more intuitive to assign bigger weights for more frequent words. For instance, the weights might be derived from a corpus based on the frequency of the words. A corpus to be used in this situation needs to be large enough to contain all the words or at least most of the words of the Swadesh list. While this would be a sound approach, frequency is not considered in the current research because such large corpora unfortunately do not exist for all the Arabic varieties under consideration yet. Therefore, all words are weighted equally for the purposes of the present study. Table 3.1 summarizes the results of the measure of lexical variation between the varieties of Arabic based on the words elicited by the participants including words they provided based on the English word and English context sentence along with the words they provided based on the data from other participants. Table 3.2 shows the results based on the words that the participants provided before they were shown what other participants provided. This shows the effect of incorporating the extra step of asking the participants about the words provided by other participants. Mainly, this caused the amount of linguistic variation to become smaller for most pairs of varieties to different degrees. I believe that this step is necessary to reliably measure linguistic variation and will be included in the following measure of linguistic variation. Table 3.3 shows the amount of lexical variation between the participants. 45  Table 3.1 The lexical variation metric between the varieties of Arabic Hearer EA GA LA MA MSA EA 0.17 0.10 0.28 0.14 GA 0.21 0.14 0.26 0.15 Speaker LA 0.16 0.15 0.27 0.13 MA 0.30 0.23 0.24 0.22 MSA 0.19 0.14 0.12 0.25 Table 3.2 The lexical variation metric between the varieties of Arabic based on words provided only by the English form of the Swadesh list Hearer EA GA LA MA MSA EA 0.19 0.15 0.31 0.14 GA 0.23 0.15 0.29 0.14 Speaker LA 0.19 0.15 0.29 0.12 MA 0.32 0.27 0.27 0.23 MSA 0.22 0.17 0.16 0.29 Table 3.3 The lexical variation metric between the participants in the experiment Hearer EA01 EA02 GA01 GA02 LEV01 LEV02 MOR01 MOR02 MSA EA01 0.04 0.21 0.26 0.17 0.17 0.30 0.32 0.13 EA02 0.04 0.20 0.25 0.16 0.16 0.29 0.30 0.14 GA01 0.22 0.22 0.18 0.16 0.20 0.25 0.27 0.13 Speaker GA02 0.23 0.23 0.14 0.20 0.22 0.30 0.30 0.17 LA01 0.19 0.18 0.15 0.24 0.12 0.27 0.28 0.12 LA02 0.20 0.19 0.19 0.25 0.12 0.31 0.32 0.13 MA01 0.31 0.30 0.24 0.32 0.25 0.29 0.05 0.19 MA02 0.33 0.31 0.26 0.33 0.27 0.30 0.05 0.23 MSA 0.22 0.22 0.18 0.26 0.17 0.18 0.29 0.29 46  EA, LA and GA are closer to each other while MA seems to be more distant from them. As a generalization, we observe from these tables that geographically proximate languages are also lexically more similar based on the lexical variation metric. Considering at the amount of lexical variation between speakers of the same variety, we also observe that the closest pair of participants is the EA speakers since they are from the same city. The MA participants are from different cities in the same country but they are also close to each other lexically. On the other hand, the two GA participants are not as close to each other, compared to the EA and MA participants. The GA participants are from different countries although their cities are close to each other. Similar to the GA participants, the LA participants are also less close to each other. They are from two distant cities located in two countries. As a generalization, based on the limited number of participants in this study, speakers from different countries tend to be linguistically more distant. This generalization should be further investigated by considering more speakers from more diverse geographic distances and from more locations. Additionally, the asymmetry of the measurement manifests itself when we compare the amounts of lexical variation between some pairs of varieties. For example, when comparing the amount of lexical variation between EA speakers and GA hearers and contrast it with the amount of lexical variation between GA speakers and EA hearers. Such difference in lexical variation could be because the different varieties may have different inventories of lexical items that would facilitate the comprehension of those lexical items in another variety. For example, if a speaker of one variety knows two words for an item on the list, he/she would fully understand a variety that uses only one of those words, but a speaker of that second variety would only understand a speaker from the first variety half of the time. 47  The amounts of lexical variation between EA speakers and hearers of GA, LA, and MA are less than the amounts of lexical variation between EA hearers and speakers from the corresponding varieties. For example, the amount of lexical variation between EA speakers and GA hearers (0.17) is less than the amount of lexical variation between EA hearers and GA speakers (0.21). This mirrors a pattern of intelligibility we observe regarding communication between Egyptian speakers and members of other varieties; the Egyptian speakers are understood better than they understand members of other varieties. Most of the time, this prompts speakers from other varieties to accommodate for Egyptian speakers. Additionally, we observe from the data that the amounts of lexical variation between LA hearers and speakers of EA, GA, and MA are less than the amounts of lexical variation between LA speakers and hearers from the corresponding varieties. For example, the amount of lexical variation between LA hearers and EA speakers (0.10) is less than the amount of lexical variation between LA speakers and EA hearers (016). This might imply that members of the LA variety are able to understand members of other varieties better than the other varieties understand them. The results also show that the closest variety to MSA is LA for both the hearers and the speakers, followed by EA and GA: GA is closer to MSA for hearers while EA is closer to MSA for speakers. The farthest from MSA is MA. However, the significance of the differences among some of those measurements is questionable. The next chapter reports on a more fine-grained measure of linguistic variation with more detailed analysis of the reliability of the measure. 48  CHAPTER 4 MEASURES OF LEXICAL AND PRONUNCIATION VARIATION BASED ON PHONE STRINGS In this chapter, I consider the phonemic representation to develop a measure of lexical variation and a measure of pronunciation variation by comparing the IPA transcription of the words of the Swadesh list. Comparing all words of the Swadesh list results in a measure of lexical variation that takes into account pronunciation variation (Section 4.1). Comparing only pairs of cognate words results in a measure of pronunciation variation (Section 4.2). Each IPA symbol in the transcription string of a word is considered as an encapsulated unit; the phonetic differences are not taken into consideration. In Chapter 5, I consider the phonetic details at a deeper level of analysis. The Levenshtein distance algorithm (Levenshtein 1966) provides a measure of sequence similarity. It was invented to measure the similarity between two binary words – a binary word is a sequence of 0s and 1s – for the purposes of detecting distortion of binary data transmitted over a channel. In addition to computer science and engineering, this algorithm has been used in linguistics (Kessler 1995; Heeringa 2004; Serva and Petroni 2008, among others) and biology (Fitch and Margoliash 1967, among others) to measure the similarity between two sequences – a transcription of a word is an instance of a sequence. This algorithm offers a framework for providing a measure of lexical variation that is more fine-grained than the measure of lexical variation discussed in the previous chapter. Many factors favor the use of the Levenshtein algorithm. First, it is applicable to any sequence, which makes it available to more than one field – linguistics and biology are two 49 relevant examples. Second, it can solve the problem in a computationally efficient time O(m×n), where m and n stand for the length of the two strings. The major improvements on the efficiency of the Levenshtein algorithm are the ability to approximate the results of the algorithm rather than calculate it precisely.13 Such improvements of the efficiency of the algorithm are not necessary for linguistic research because the strings under consideration are short, which makes any improvement in the efficiency negligible. Third, it is expandable through the dynamic design of the algorithm. There are two main dynamic aspects of the algorithm: it divides the string into substrings with the substrings being prefixes by default, and it keeps the cost of the basic operations, to be detailed below, independent of the algorithm itself. This specific feature makes the algorithm applicable to linguistic research. I will also propose a new technique utilizing this feature of the algorithm later in this thesis. Fourth, it can be improved to find the best alignment between two strings. This is achieved by keeping track of the places of the insertions, deletions, and substitutions. The Levenshtein distance algorithm can be defined as a similarity metric that finds the minimum number of insertions, deletions, and/or substitutions needed to transform one string to another. Insertions, deletions and substitutions are called the basic operations of the algorithm. In its most trivial case the cost of each of these operations is set to one. It is also possible to set different costs, and changing the costs might have dramatic effects on the variation metric. In the current chapter, I am setting the cost of basic operations to one, while the next chapter proposes a model of sound representation from which the cost of the basic operations are derived. 13 For more details see Navarro (2001), Ukkonen (1983), Ukkonen (1985), and Berghel and Roach (1996). 50  Kessler (1995) was among the first to use the Levenshtein distance algorithm to measure dialect distances. His main objective was to identify the grouping of the Irish Gaelic dialects and to determine the linguistic boundaries between them. Kessler used part of a linguistic atlas developed by Wagner (1958). This part contained a list of 51 concepts represented by 312 different words or phrases. The concepts were presented in narrow transcription based on the IPA standard. Kessler ran different types of distance metrics that can be divided into two groups. The first group consisted of variation metrics on the lexical level based on the etymon identity and word identity, similar to what was discussed in Chapter 3. The second group of variation metrics considered the IPA transcription and calculated the distance based on the Levenshtein distance algorithm. Within the second group, Kessler introduced a method of phone string comparison. This method was based on the Levenshtein distance with the default cost of the basic operations where all insertions, deletions, and substitutions were set to one. Another technique within the second group was to incorporate the phonetic features in the cost of the basic operations. This technique is called feature string comparison. Kessler compared the correlation of the variation metrics with the traditional approach of counting the number of isoglosses between dialect sites in a dialect map. The variation metrics based on the Levenshtein distance algorithm outperformed the etymon identity and word identity methods. Within the methods based on the Levenshtein distance algorithm, the phone string comparison method outperformed the methods that considered calculating phonetic differences. Kessler did not conclude that phonetic variation is irrelevant. Rather, he highlighted the importance of further developing methodologies that incorporate phonetic features in the variation metric. 51  Serva and Petroni (2008) introduced the idea of normalizing the Levenshtein distance between a pair of words over the length of the longer word. This helped ensure that all lexical items are contributing the same weight to the variation metric. The distance between a pair of languages would then be the average of the normalized distances between lexical items. The cost of insertions, deletions, and substitutions were all set to one. The normalization over the length of the longer word generated a distance metric that is less than one for any pair of words which in turn, entailed that the contribution of each lexical item is guaranteed to be less than or equal to one. In other words, all lexical items have the same potential to contribute to the variation metric; assigning weights to lexical items based on frequency was not considered in their study. Serva and Petroni (2008) used a list of 200 words from 50 languages. Some lists were missing some words, but the maximum number of missing words did not exceed 13. The words were transcribed in English orthography. Based on the known divergence times between two pairs of languages, Serva and Petroni retrieved the divergence times between all other pairs of languages and built a language tree that included the divergence times of all languages in consideration. Previous studies have also built language trees, such as Gray and Atkinson (2003) and Gray and Jordan (2000), but instead of using Levenshtein distance, they focused on the number of non-cognate words as a variation metric. The use of the Levenshtein distance algorithm has been widely accepted in Linguistics since Kessler (1995). The algorithm was further improved by Serva and Petroni (2008) and Wichmann et al. (2010) by introducing the idea of normalizing the distances. However, their improvements were found to be useful only when comparing distantly related languages. On the other hand, the Levenshtein distance algorithm can be improved by modifying the cost of 52 insertions, deletions, and substitutions based on sound relatedness or phonetic details. This is one of the main contributions of this thesis, as discussed in detail in chapter 5. 4.1 Measure of lexical variation at the phonemic level I developed an algorithm to measure the lexical variation based on the IPA transcription of the words of the Swadesh list as transcribed in Appendix A. The algorithm uses the Levenshtein distance algorithm with one as the cost of the basic operations. For each Swadesh list item, the algorithm goes through the words provided by the speaker. For each of those words it finds the closest cognate word provided by the hearer for the same Swadesh list item. The assumption is that the hearer is matching the speaker‟s word to the closest word in his/her lexicon, and for communication to be successful, both words should have the same meaning i.e. belong to the same Swadesh item. For example, a GA speaker trying to communicate the meaning of the word „because‟ (Swadesh item number 206) by using the word ʕaʃAn that exists in his/her lexicon with an EA hearer who has two cognates of this word in his/her lexicon ʕaʃAn and ʕalaʃAn. In such a case, the EA hearer is interpreting the speaker‟s word to the closest in his/her lexicon which is ʕaʃAn. For this pair of varieties the existence of the word ʕalaʃAn in the hearer‟s variety does not contribute to the amount of linguistic variation. This component of algorithm also contribute to the asymmetry of the measure because on the other direction of the communication an EA speaker will also be using the word ʕalaʃAn which a GA hearer will match to ʕaʃAn which has a distance of two deletions. Following Serva and Petroni (2008), I normalize the output of the Levenshtein distance algorithm for each pair of words over the length 53 of the longer word.14 Then, I normalize over the length of the list. These steps generate a distance that is guaranteed to be less than or equal to one. This ensures that the results of the variation metric are comparable even if some varieties tend to have longer words or if some varieties have longer or shorter lists of pairs words. The algorithm is provided in Figure 4.1. The results of the measure of lexical variation based on phone strings align with the results of the lexical variation based on non-cognate words provided in the previous chapter. The closest varieties to each other are also the closest geographically: EA, LA and GA. On the other hand, MA seems relatively more distant. The results of this measure also show the two patterns of asymmetry reported by the previous measure. First, the amounts of variation between EA speakers and hearers of GA, LA, and MA are less than the amounts of variation between EA hearers and speakers from the corresponding varieties. Second, the amounts of variation between LA hearers and speakers of EA, GA, and MA are less than the amounts of variation between LA speakers and hearers from the corresponding varieties. It would be interesting to see if those two patterns of asymmetry hold for the pronunciation variation metrics developed in Section 4.2 and chapter 5. The results are given in Table 4.1. Reliability is an important factor for any measurement procedure. In this study, I provide two tests of the reliability of the algorithm in Figure 4.1. The first test aims to provide a visual realization of the stability of the measure given the size of the Swadesh list. In other words, is the size of the Swadesh list large enough to confidently determine the amount of lexical variation between the varieties under consideration? To answer this question, for each pair of varieties I 14 Normalizing over the length of the words is an advantage computationally so that each word contributes equally to the computation. However, linguistically there may be reasons to consider developing an algorithm sensitive to word length in future research. One reason is that there does not seem to be an established theoretical definition of word Haspelmath (2011), as well as situations where, for example, the average length of words in one variety is shorter than the average length of words in another and this might contribute to the overall difference between these two varieties. 54 ran a convergence exercise by starting with a list of one randomly selected item. I calculated the amount of variation according to the algorithm described Figure 4.1. I repeated the calculation after growing the list in steps of one randomly selected item. Figure 4.2 shows that the bigger the size of the list is, the more stable the amount of variation between varieties would be. Note that I show a subset of the pairs of varieties in this figure because of the limited space; other pairs of varieties show similar patterns. Based on the patters of convergence, we do not expect that increasing the size of the list would dramatically change the results. The second test of reliability is based on statistical tools. Assuming that the Swadesh list is a randomly selected sample from the lexicons of the relevant varieties and that the amount of lexical variation between the pairs of words forms a normal distribution, we can use statistical tools to provide a confidence interval for each of the findings reported in Table 4.1. Table 4.2 summarizes the range of 95% confidence intervals for all pairs of varieties. Based on the items of the Swadesh list there is 95% confidence that if we randomly selected similar sized list from the lexicon then the amount of lexical variation between the varieties would fall in the ranges reported in Table 4.2. To assess the closeness of the local varieties to MSA, we focus on the ability of the members of local varieties to comprehend MSA where the speaker belongs to MSA and the hearer belongs to one of the local varieties. Looking at the ranges of the amounts of lexical variation between hearers of the local varieties – EA, GA, LA, and MA – and MSA speakers (highlighted in Table 4.2), we see that MA is more distant relative to MSA than the other local varieties are. Also, GA is closer to MSA than EA is. It could be argued that LA is closer to MSA than EA is and more distant than GA is. However, the ranges overlap and different datasets could provide different results. This variation metric, as the confidence intervals show, did not determine which local variety is closer to MA, as all intervals referring to 55 MA as either speaker or hearer overlap. On the other hand, it shows that EA speakers are closer to LA hearers than GA hearers are. This might imply that EA is understood by LA better than GA. Moreover, GA speakers are closer to LA hearers than EA hearers are. This also might imply that GA is understood by LA better than EA. On the other hand, the overlapping confidence intervals for the amounts of variation between LA speakers and EA hearers and between LA speakers and GA hearers imply that we are not able to confidently distinguish the closeness of EA and GA hearers to LA speakers based on this measure of lexical variation. Table 4.1 Results of the lexical variation metric based on the phone string Hearer EA GA LA MA MSA EA 0.32 0.24 0.51 0.36 GA 0.40 0.27 0.50 0.32 Speaker LA 0.35 0.31 0.51 0.37 MA 0.52 0.48 0.46 0.51 MSA 0.38 0.28 0.31 0.52 Figure 4.1 Algorithm used to measure the lexical variation based on the phone strings of the words of the Swadesh list int Lexical_variation_metric_based_on_phone_string (speaker language as LangA, Hearer language as LangB) { Distance_acc = 0 Word_count = 0 For each Swadesh_item in the SwadeshList { Get wordsA_list from LangA that belongs to Swadesh_item Get wordsB_list from LangB that belongs to Swadesh_item For wordA in wordsA_list { Get wordB from wordsB_list that is closest to wordA based on Levenshtein dist. d = Levenshtein(wordA, wordB) d = d / max(length(wordA, wordB)) Distance_acc = Distance_acc + d Word_count = word_count + 1 } } Distance = Distance_acc / word_count Return Distance } 56 Figure 4.2 The convergence of the lexical variation metric based on the phone strings, X-axis shows the number of pairs of lexical items in the list. The number of items increases in steps of one. The Y-axis shows the amount of variation based on the algorithm described in Figure 4.1. This figure shows the pattern of convergence for a subset of the pairs of varieties; other pairs show a similar pattern. 1.2 1 0.8 0.6 EA to MA GA to MA 0.4 GA to EA EA to MSA EA to GA GA to MSA GA to LA EA to LA 0.2 0 222 313 105 118 131 144 157 170 183 196 209 235 248 261 274 287 300 326 339 352 365 378 391 1 14 27 40 53 66 79 92 57  Table 4.2 The range of 95% confidence level of the lexical variation metric between the pairs of varieties Speaker- Degrees of Mean of Range of 95% confidence interval Hearer freedom normalized distance EA-GA 257 0.32 0.28 - 0.36 EA-LA 257 0.24 0.21 - 0.28 EA-MA 257 0.51 0.47 - 0.54 EA-MSA 257 0.36 0.32 - 0.4 GA-EA 351 0.4 0.36 - 0.43 GA-LA 351 0.27 0.24 - 0.3 GA-MA 351 0.5 0.47 - 0.53 GA-MSA 351 0.32 0.29 - 0.36 LA-EA 394 0.35 0.32 - 0.39 LA-GA 394 0.31 0.28 - 0.34 LA-MA 394 0.51 0.48 - 0.55 LA-MSA 394 0.37 0.35 - 0.4 MA-EA 272 0.52 0.48 - 0.56 MA-GA 272 0.48 0.44 - 0.52 MA-LA 272 0.46 0.42 - 0.5 MA-MSA 272 0.51 0.47 - 0.55 MSA-EA 269 0.38 0.34 - 0.42 MSA-GA 269 0.28 0.25 - 0.32 MSA-LA 269 0.31 0.28 - 0.35 MSA-MA 269 0.52 0.48 - 0.55 4.2 Measure of Pronunciation variation at the phonemic level The lexical variation metric reported in the previous section was based on comparing the IPA transcription of pairs of both cognate and non-cognate words. It might be considered problematic to compare the pronunciation of unrelated non-cognate words. But in this case, it is legitimate to do so because the resulting measure – discussed in the previous section – estimates lexical variation based on the phone string across all words, cognate and non-cognates, rather than purely pronunciation variation within cognates as calculated in this section. 58  In an effort to measure the amount of pronunciation variation, I developed an algorithm similar to the algorithm developed in the previous section except that the comparison of phone strings is limited to pairs of cognate words. This is achieved by incorporating the manually identified cognate words that were developed for the lexical variation metric discussed in Chapter 3. The algorithm is shown in Figure 4.3. The algorithm takes into consideration only pairs of words that are identified as cognates and keeps track of the number of considered pairs of words to normalize over the length of the list. The results of the measure of pronunciation variation based on the phone strings are given in Table 4.3. Similar to the measures of lexical variation, we still see that EA, LA and GA are closer to each other while MA seems to be more distant from them. Moreover, we still see the pattern of asymmetry for EA speakers: the amounts of variation between EA speakers and hearers of GA, LA, and MA are less than the amounts of variation between EA hearers and speakers from the corresponding varieties. This could imply that members of the EA variety are understood by other speakers better than they understand them. The other pattern of asymmetry for LA speakers is also valid – similar to the lexical variation metric reported in the previous section. The amounts of variation between LA hearers and speakers of EA, GA, and MA are less than the amounts of variation between LA speakers and hearers from the corresponding varieties. This could imply that members of the LA variety understand members of other varieties better than the other varieties understand them. Table 4.4 shows the degrees of freedom, margins of error, and the 95% confidence intervals for the amounts of variation between the varieties according to the current measure of pronunciation variation. Comparing the ranges for the pronunciation variation between MSA speakers and hearers from the local varieties (highlighted in Table 4.4), we notice that GA is the closest followed by LA and EA. Similar to the lexical 59 variation metric based on phone strings, there is still an overlap for the 95% confidence intervals for MSA-LA and MSA-EA. Moreover, MA is still the farthest to MSA. As for the local varieties, MA hearers are closer to EA speakers than both GA and LA speakers, with no significant distinction between GA-MA and LA-MA. Moreover, due to the overlap of confidence intervals, there is no distinction with regard to the amount of pronunciation variation based on phone strings between MA speakers and hearers of EA, GA, and LA. Similar to the lexical variation metric reported in the previous section, EA speakers are closer to LA hearers than GA hearers and GA speakers are closer to LA hearers than EA hearers. Also, there is no distinction about the closeness of EA hearers and GA hearers to LA speakers. Figure 4.4 summarizes the results of the lexical and pronunciation variation metrics based on the phone string. This plot is provided to make the comparison between the two variation metrics easier. It shows that the results of the pronunciation variation are parallel the results obtained by the lexical variation metric based on the phone string. One area for improvement is to incorporate phonetic features in the measure of pronunciation variation. This is taken up in the next chapter. Table 4.3 Results of the pronunciation variation metric based on phone strings Hearer EA GA LA MA MSA EA 0.20 0.16 0.34 0.26 GA 0.23 0.16 0.35 0.21 Speaker LA 0.22 0.19 0.35 0.28 MA 0.35 0.33 0.31 0.38 MSA 0.25 0.17 0.22 0.37 60 Table 4.4 95% confidence intervals for the measure of pronunciation variation based on phone strings between pairs of varieties Speaker- Degrees of Mean of Range of 95% confidence interval Hearer freedom normalized distance EA-GA 206 0.2 0.17 - 0.23 EA-LA 226 0.17 0.14 - 0.19 EA-MA 176 0.35 0.31 - 0.38 EA-MSA 214 0.26 0.23 - 0.3 GA-EA 258 0.24 0.21 - 0.27 GA-LA 288 0.16 0.14 - 0.18 GA-MA 241 0.35 0.32 - 0.38 GA-MSA 282 0.21 0.18 - 0.23 LA-EA 308 0.23 0.2 - 0.25 LA-GA 314 0.19 0.17 - 0.21 LA-MA 266 0.35 0.32 - 0.37 LA-MSA 327 0.29 0.26 - 0.31 MA-EA 183 0.36 0.32 - 0.4 MA-GA 199 0.34 0.3 - 0.37 MA-LA 202 0.31 0.28 - 0.35 MA-MSA 204 0.39 0.35 - 0.42 MSA-EA 205 0.25 0.22 - 0.29 MSA-GA 218 0.17 0.15 - 0.2 MSA-LA 228 0.22 0.2 - 0.25 MSA-MA 188 0.37 0.33 - 0.4 61 Figure 4.3 Algorithm used to measure the pronunciation variation based on phone strings of cognate words in the Swadesh list int Pronunciation_variation_metric_based_on_phone_string (speaker language as LangA, Hearer language as LangB) { Distance_acc = 0 Word_count = 0 For each Swadesh_item in the SwadeshList { Get wordsA_list from LangA that belongs to Swadesh_item Get wordsB_list from LangB that belongs to Swadesh_item For wordA in wordsA_list { Get wordB from wordsB_list that is closest to wordA based on Levenshtein dist. If wordA and wordB are cognates { d = Levenshtein(wordA, wordB) d = d / max(length(wordA, wordB)) Distance_acc = Distance_acc + d Word_count = word_count + 1 } } } Distance = Distance_acc / word_count Return Distance } Figure 4.4 Results of the lexical and pronunciation variation metrics based on the phone strings 0.6 0.5 0.4 0.3 Lexical 0.2 Phonemic 0.1 0 MSA-EGY GLF-EGY GLF-MOR LEV-EGY EGY-MOR GLF-MSA LEV-MSA MOR-GLF MOR-MSA MSA-MOR EGY-MSA GLF-LEV LEV-MOR MOR-LEV MSA-LEV MSA-GLF EGY-LEV LEV-GLF EGY-GLF MOR-EGY 62  CHAPTER 5 MEASURES OF PRONUNCIATION VARIATION BASED ON THE MATHEMATICAL REPRESENTATION OF SOUND This chapter discusses the approach and methodology I follow to develop the measures of pronunciation variation based on phonetic features. As mentioned in chapter 4, one of the favorable features of the Levenshtein distance algorithm is its ability to set variable costs for the basic operations – insertions, deletions, and substitutions. This allows the incorporation of more linguistic details by setting the cost of the basic operations based on phonetic features. For example, the cost of replacing the phoneme /s/ by /z/ should be less than the cost of replacing /s/ by /k/, given that the first pair differs only in voicing while the latter involves more phonetic differences. As mentioned earlier, Kessler (1995) introduced the use of the Levenshtein distance algorithm to measure linguistic variation. He compared different approaches to compute the distances between Irish Gaelic dialects and compared these with the traditional method: counting isoglosses within a dialect map. Under one of the approaches, he used the Levenshtein distance algorithm with the default cost of one as the cost of the basic operations. Under another approach, he incorporated differences in phonetic features to calculate the cost of the basic operations. He used a set of twelve phonetic features – nasality, stricture, laterality, articulator, glottis, place, palatalization, rounding, length, height, strength, and syllabicity. The values of each feature were set as discrete ordinal values between 0 and 1, with the exact values being arbitrary. Thus, the cost of replacing one phone with another was calculated as the average of the differences between all phonetic features representing those two sounds. Kessler found that the 63 simpler phoneme-based method with the default cost of basic operations outperformed the multivalued phonetic features method in comparison to the traditional method of counting the number of isoglosses between dialect sites in a dialect map. According to Kessler, the low performance may be due to the arbitrariness of assigning values to the phonetic features. Heeringa (2004) also used the Levenshtein distance algorithm to calculate the distance between dialects. His study covered a wide variety of ways to calculate the cost of the basic operations. They are divided into two basic categories. The first category is based on phonetic features and the second category is based on the acoustic representation. Within the first category, there are three phonetic feature systems derived from different studies – one based on Vieregge et al. (1984) and Cucchiarini (1983), one based on Almeida and Braun (1986), and one based on Hoppenbrouwers and Hoppenbrouwers (2001). The cost of insertions and deletions is calculated based on the distance between the phoneme and silence while the cost of substitutions is calculated based on the distance between the pair of phonemes being substituted. The distance is derived from segment representation according to the corresponding phonetic representations (Heeringa 2004, p. 124). The methods using acoustic-based representations did not perform as well as the methods using phonetic features. The phonetic feature systems that Heeringa (2004) used were similar in principle to what Kessler developed in his 1995 study. They both represent phonetic segments by a set of phonetic features and each phonetic feature is associated with a set of ordinal numbers. The differences between them are related to the number of features and the ordinal values assigned to each phonetic feature to distinguish phonetic segments15. 15 See Heeringa (2004) section 3.1 for more details. 64  What is counterintuitive is that both Kessler and Heeringa found that disregarding all phonetic details and using the default cost of one for the Levenshtein algorithm‟s basic operations produced better results. This finding does not necessarily mean that discarding phonetics details is better but instead may derive from the way costs were assigned, as suggested by Kessler. Thus, such tools have to be designed carefully and should include information about the patterns of sound change that leads to variation. Gooskens (2007) compared the correlation between the lexical distance and the degree of mutual intelligibility with the correlation between the phonetic distance and the degree of mutual intelligibility. Gooskens found that mutual intelligibility is more correlated with phonetic distance than with lexical distance. He used Heeringa (2004) as a basic phonetic distance metric. Kondrak (2003) incorporated a new idea in the Levenshtein distance algorithm. In addition to insertions, deletions and substitutions, he introduced the operation of expansion and compression, where a phonetic segment can be expanded or compressed for a specific cost. For each of the 13 phonetic features that he used, he specified a weight, or what he called the salience of the feature, and whether the feature can be applied to vowels and/or consonants. In contrast to the arbitrary nature of the ordinal values that Kessler assigned to his feature set, Kondrak assigned his ordinal values based on physical measurements where applicable. The physical measurements were taken from Ladefoged (1975). The weights assigned to the phonetic features were not based on any physical measurements. Kondrak compared his algorithm with others in terms of its ability to identify cognate words. The comparison included the methods from Kessler (1995), Covington (1996), Somers (1998), Gildea and Jurafsky (1996), Nerbonne and Heeringa (1997), and Oaks (2000). Kondrak‟s algorithm outperformed them all. 65  Following this line of research by Kessler (1995) and others, I use the Levenshtein distance algorithm that these researchers have shown to be applicable to measuring pronunciation variation by incorporating phonetic details. The next step is to design a technique to calculate the cost of the basic operations independent of the researcher‟s intuitions. To achieve this goal, we need to address the following questions: 1. How is the cost of substitutions based on phonetic features derived? How is the cost of insertions and deletions set? 2. What are the sets of phonetic features to be incorporated in representing phones? How are ordinal numbers assigned to values in the phonetic feature sets? How do we assign weights for the different phonetic features? 3. How do we determine if a set of values and weights of phonetic features are better or worse than another set of values and weights? How do we reach the optimal set of values and weights? 5.1 The mathematical representation of sound This section formalizes a layer of computational representation of sound that is more abstract than the acoustic representation and more detailed than the phonemic representation. The necessity for the new layer of representation of sound comes from the need for an interface that communicates phonetic features that can derive a measure of phonetic similarity. Such an interface is useful in measuring pronunciation variation. At the more abstract level of sound representation, a phonemic based representation, each phoneme is considered as an entity that hides the phonetic features and the fluctuations of the air pressure produced by a speaker uttering the sound. At the proposed layer of representation – the mathematical representation – phonetic 66 features are encoded. At the more detailed level of representation, the acoustic representation, the fluctuations of the air pressure are recorded over time. Which does not provide a direct interface to communicate the phonetic features. As stated in the introduction of this thesis, one of the goals for this study is to enhance our understanding of the components of sounds. I am mainly concerned with addressing two questions: (i) What are the key components of sound? and (ii) To what degree is each component playing a role in measuring the similarities and differences of pairs sounds? Answering these two questions is key for developing a measure of pronunciation variation that is more fine-grained than the measure of pronunciation variation based on phone strings (Chapter 4). In addition to the importance of answering these questions to developing a measure of pronunciation variation, their answers might carry potential improvements to some NLP tasks (Chapter 6). Also, they help us provide an empirical framework to answer the theoretical questions about the components of sounds in phonetics and phonology. That said, the specific focus of this study is the development of a measure of pronunciation variation while leaving the additional potential applications for future research. The quantifiability of pronunciation variation between two sounds is key to the design of the mathematical representation of sound. If each phoneme is represented as a point in a space, then the amount of pronunciation variation between two phonemes is directly derived from the Hamming distance between the points.16 Within such a design, we need to find the dimensions of the space and the basic principle behind positioning points in the space. For mathematical 16 Hamming distance is more applicable than Euclidian distance: the former better reflects the changing phonetic features because the latter allows for diagonal shortcuts, increasingly limiting the effect of each individual dimension as the number of dimensions increases. Hamming distance measures the total number of steps on any axis required to reach one point from the other. In other words, the distance is calculated as if a car were to drive around city blocks to reach its destination rather than as the direct path a bird would fly between the points. 67 simplicity, we assume that each phonetic feature is an independent factor; therefore each phonetic feature corresponds to a dimension in the space. 5.1.1 The phonetic features for encoding in the mathematical representation of sound The mathematical representation of sound must encode a set of phonetic features that distinguishes all phonemes in the phonemic inventory of the varieties under consideration. But should not include any extraneous features for reasons of computational efficiency. Two frameworks in phonology inspired the set of features used here: the articulatory phonology framework highlights the importance of articulatory gestures, while autosegmental phonology highlights the importance of phonetic features. A purely articulatory model would complicate what could simply be viewed as a phonetic feature. For example, the emphatic feature in Arabic is expressed by set of articulatory gestures including backing the root of the tongue, sagging of the middle of the tongue, and slight rounding of the lips (Abunasser et al. 2011). The complex set of articulatory gestures can be represented as one phonetic feature. On the other hand a purely autosegmental model would fail to capture the relatedness of sound in terms of place of articulation and manner of articulation in a computationally effective way. Drawing from both phonological frameworks and keeping in mind computational simplicity and efficiency, I propose a hybrid model: each phoneme is represented by one main articulatory gesture, while secondary articulatory gestures are considered to produce phonetic features. The resulting model is a representation of sound that can be used computationally in an effective way. The main articulatory gesture is represented by a place of articulation and the degree of constriction at the place of articulation, which allows efficient comparison of different sounds for this core property. The phonetic features are voicing, nasality, laterality, trill/flap, emphasis, lip rounding, 68 affrication, gemination, and vowel length. A list of the phonemes for the varieties under consideration along with the details about the encoding is provided in Appendix B. The set of phonetic features being used here are derived from the IPA table and are the minimum features required to encode all sounds in the varieties under consideration; other languages might require additional or fewer features. Developing a universal set of features is most likely possible but not necessary at the current stage; furthermore, it increases the computational complexity of other components of the project (see Section 5.1.3). As mentioned earlier, each phoneme is represented as a point in a multidimensional space where the coordinates of the point specify the main articulatory gesture and the phonetic features. The first dimension specifies the place of articulation of the main articulatory gestures while the second dimension specifies its degree of constriction. The remaining dimensions correspond to the phonetic features, where each feature has its own dimension. The values for each phonetic feature are set to 0 or 1 depending on whether the feature is manifested in the sound or not. The values in the first dimension (the place of articulation of the main articulatory gesture) correspond to glottal, pharyngeal, uvular, velar, central-vowel, palatal, post-alveolar, alveolar, dental, labiodental, and bilabial, distributed in the range from 0 to 1 in increasing order. Without a phonetically motivated reason for assigning specific values to each intermediate place of articulation, I am proposing a technique that defines the values of the places of articulation as parameters of the representation of sound that will be represented by calculations specific to each pair of varieties (see section 5.1.3). 69  The second dimension defines the degree of constriction at the place of articulation of the main articulatory gesture. Following the same guidelines of the place of articulation, the smallest value for the degree of constriction corresponds to stops and the largest value corresponds to the degree of constriction of the low vowel; the full range of values is as follows: stop (0), fricative, approximant, high-vowel, mid-vowel, low-vowel (1). The exact values corresponding to the degrees of constriction are parameters that are defined in the following subsections. In previous studies (Kondrak 2003; Heeringa 2004, among others), the consonant and vowel distinction is represented by two separate categories. However, in the current study, the distinction is derived by a gap between the two categories in the second dimension, which is parallel to how the distinction is physically realized (Stevens 2000) and also allows for the computational model to capture the similarity between consonants and vowels, which, for example, can assimilate to one another and otherwise interact. 5.1.2 Parameters and weights of the mathematical representation of sound The set of phonetic features encoded in the mathematical representation of sound represent each phone as a point in a multidimensional space where the coordinates of the point encode the values of all features. The range of each dimension is 0 to 1. This design results in a computationally effective method to capture sound relatedness. The phonetic distance between a pair of phones can be calculated as the distance between the points representing them. On the other hand, such a design implies that all phonetic features have equal importance because they all have the same range (0 to 1). This problem is resolved by setting weights for all dimensions. The computational component that allows the dimensions to be scaled by the weights needs to be independent of the variation metric and independent of the researcher‟s intuitions – derived 70 based on computational calculation. The scaling factors of the dimensions are referred to as weights in the rest of this thesis. Before the Hamming distance between the points is calculated, the axis for each dimension is scaled based on the assigned weight. The first two dimensions are multivalued where places of articulation and degrees of constriction are expressed as the values of the relevant coordinates between 0 and 1. The exact values of the places of articulation and degrees of constriction are, similarly, to be determined independent from the variation metric and independent from the researcher‟s intuitions. The coordinates that define the places of articulation and degrees of constriction are to be referred as the parameters of the mathematical representation of sound. However, we need to set default values of the parameters to be used as the starting point in the process of finding the ultimate values of the parameters for each pair of varieties. The default values of the parameters are assigned in a way that they are equally gapped. Table 5.1 reports the default values assigned to the parameters. Incorporating the mathematical representation of sound in the calculation of pronunciation variation using the Levenshtein distance algorithm entails that the cost of replacements is to be calculated based on the distance between the pair of points corresponding to the pair of phones in question. The question that arises in this context is the following: What is the cost of the other basic operations, insertion and deletion (to be referred as indel17)? In the new set up that involves the new method to calculate the cost of replacements, keeping the cost of indels to the default cost is not plausible. It might seem plausible to set the cost of indels to the maximum cost of replacement (or a fraction thereof) which is defined as the most distinct pair of phonemes as measured by the distance between the most distant points. However, I do not 17 The term indel has been used by Kondrak (2003) and in studies in molecular biology. 71 believe there is a convincing theoretical or logical motivation for such a decision. A computationally feasible and logically plausible solution is to deal with the problem of calculating the cost of indels in a similar method to that of the weights and parameters. The following section discusses the computational component that calculates the weights, the parameters, and the cost of indels. Table 5.1: Default values of the parameters Dimension Parameter name Default value Glottal 0 Pharyngeal 0.1 Uvular 0.2 place of articulation Velar 0.3 Central vowel 0.4 Palatal 0.5 Postalveolar 0.6 Alveolar 0.7 Dental 0.8 Labiodental 0.9 Bilabial 1 Stop 0 Fricative 0.2 constriction Degree of Approximant 0.4 High vowel 0.6 Mid vowel 0.8 Low vowel 1 72 5.1.3 Optimizing weights, parameters, and cost of indels based on their ability to identify cognates This section reports on a computational component that sets values to the Weights, the Parameters, and cost of Indels, to be referred as WPIs. Kondrak (2009) used a phonetic similarity algorithm to identify cognate words. He compared several phonetic similarity metrics based on their ability to identify cognate words. The intuitive assumption is that a better phonetic similarity metric would result with a better cognate word identification algorithm. Following the same intuitive assumption, a better set of WPIs leads to better identification of cognate words for a pair of languages. I find the optimal WPIs based on their ability to identify cognates (as defined in Chapter 3). We need a computational component that given two WPIs can identify which one is better for a pair of varieties. Based on such computational component we optimize for a better set of WPIs. The best set of WPIs is the set that is able to identify cognates the most; and hence separates cognates and non-cognates the most. In our case, given the Swadesh list for a pair of varieties, the distances between pairs of cognate words form one distribution, and the distances between non-cognate words form another distribution. A good set of WPIs would result in an average distance between non-cognate words to be higher than the average distance between cognate words. Moreover, the more distant the averages are the better the WPIs would be. The distance between the averages of the distributions and the dispersion of each distribution are the key factors that determine the separation of the two distributions. Thus, the more multiples of standard deviations that separate the averages of the two distributions, the better the set of WPIs is. The formula can be derived as: - Given a list of words, the Swadesh list on our case, for a pair of varieties. 73  o A: The distribution of the distances between cognate words. o B: The distribution of the distances between non-cognate words. o p: a point between A and B that satisfies both 5.1 and 5.2 o x: the multiplication factor of the standard deviation used in equations 5.1, 5.2, and 5.3; is referred to as the separation factor o (5.1) o (5.2) - Solving for yields o (5.3) Optimizing for a higher separation factor by setting different weights for each pair of varieties could potentially result with optimal WPIs for each pair of varieties. Such an optimization problem can be solved by implementing a hill climbing algorithm. The hill climbing algorithm consists of repeating two steps. The first step is to start with an arbitrary solution. The second step is to repeatedly improve the solution by finding a better neighboring solution. The process of trying to find a better neighboring solution is repeated until the improvement of the solution fails. Similarly, I start with randomly selected weights, a randomly selected cost of indels, and default values for the parameters. Then the value of each component of the WPIs is increased and decreased by a predefined step size and the WPIs are evaluated each time by calculating the separation factor. Then we select the neighboring WPIs that produced the highest increase in the separation factor. The process of trying to find better neighboring set of WPIs is repeated until the algorithm fails to increase the separation factor. After this point, the last WPIs are considered to produce a local maximum. Following this 74 algorithm, a local maximum is found for each randomly selected WPIs. After finding a reasonable number of local maxima or repetitions of local maxima the algorithm stops and assumes that the best local maxima is a global maxima and the corresponding set of WPIs are the optimal set of WPIs. The high computational complexity of the nature of the problem highlights three considerations to keep in mind in order to make it computationally feasible. The first is the step size. A larger step size is computationally less expensive but might lead to a premature local maximum while a smaller step size could be unrealistically computationally expensive. Given the computational resources and after investigating different values and results, the value of the step size is set to 0.1 in an initial stage. Once a local maximum is found, the step size is set to 0.01 and the process repeats one final time. The second consideration is the range from which the random values of the weights and cost of indels are selected. The range is set to the values between 0 and 5 in steps of 0.1. There are 11 weights and 1 value for the cost of indels, thus in total 12 variables to assign random starting values. For each variable, there are 51 possible points to start with, so the total number of possible values is 5112. The third consideration is when to assume that we have found enough number of local maxima and the largest local maxima generates the optimal WPIs? Ideally, we want to be as certain as possible that we have exhausted most of the local maxima and most likely, the global maximum is one of them. The standard procedure for hill climbing algorithms is to start with a pre-specified number of randomly selected starting points, and assume that the best maxima correspond to the optimal result. However, I could not find such a number that is computationally feasible and effective for all pairs of varieties. Instead, the algorithm is designed to repeat the process of starting with randomly selected starting points and find their relevant local maxima until it has five repeated 75 local maxima. Then the algorithm stops and assumes that the global maximum is the best maximum found and the corresponding set of WPIs is the optimal solution. One of the challenges to the algorithm is having the starting, randomly selected values begin in a plateau – increasing/decreasing at least one of the values of the starting WPIs will not have any effect on the separation factor. This problem is solved by decreasing the value of each weight, one at a time, as long as the separation factor is not decreasing. This brings the function to an edge of an incline where it may begin climbing and increase the separation factor. The algorithm used to calculate the WPIs is shown in Figure 5.1. Figure 5.2 illustrates the calculation of the separation factor for a pair of varieties (EA and GA). The x-axis marks the word number and the y-axis marks the distance between pairs of words. The distances between cognate words are marked by pluses and the distances between non-cognate words are marked by circles. The two dotted horizontal lines mark the averages of the two distributions. The two vertical lines mark one standard deviation below and one standard deviation above the average for each distribution. The point p is marked by the solid horizontal line. Figure 5.2 (A) shows the separation of the two distributions given the starting randomly selected values. Figure 5.2 (B) shows the separation after the step that avoids having the WPIs in a plateau. Figure 5.2 (C) shows the first change in the weights in an effort to increase the separation factor, there is a step up for the nasal and emphatic features and a stem down for affricate feature. Next, the algorithm adjusts the places of articulation and degrees of constriction. For this pair of varieties and the initial set of WPIs, the algorithm need 53 steps to find a local maxima, the WPIs of the local maxima is given in Figure 5.2 (D). 76  Figure 5.1 Algorithm used to optimize the WPIs Pseudocode Levenshtein(wpi_set, wordA, wordB) { returns the distance between wordA and wordB following the Levenshtein distance algorithm by setting the cost of the basic operations using the given wpi_set } Pseudocode separation_factor(wpi_set, LangA, LangB) { cognate_list = [] non_cognate_list = [] For each Swadesh_item in the SwadeshList { Get wordsA_list from LangA that belongs to Swadesh_item Get wordsB_list from LangB that belongs to Swadesh_item For wordA in wordsA_list { For wordB in wordsB_list { d = Levenshtein(wpi_set, wordA, wordB) d = d / max(length(wordA, wordB)) If wordA and wordB are cognates cognate_list.add(d) else non_cognate_list.add(d) } } } return (average(non_cognate_list)-average(cognate_list)) /(std(cognate_list)+std(non_cognate_list)) } Pseudocode optimize_parameters(wpi_set, LangA, LangB) { [variables,parameters] = wpi_set do { step_base_sep_factor = separation_factor(wpi_set, LangA, LangB) best_wpi_set = wpi_set best_step_sep_factor = 0 For parameter_dim in [parameters.places_of_articulation, parameters.degrees_of_constriction] { for parameter in parameter_dim { For all possible values of parameter preserving ordinality { wpi_set = [x,parameters] sep_factor = separation_factor(wpi_set, LangA, LangB) if sep_factor > best_step_sep_factor { best_step_sep_factor = sep_factor best_wpi_set = wpi_set } } } } step_end_sep_factor = separation_factor(best_wpi_set, LangA, LangB) }while(step_end_sep_factor > step_base_sep_factor) } Pseudocode get_initial_WPIs_set() { variables = get 12 random values in the range 0, 0.1, 0.2 … 5 parameters.places_of_articulation = default places of articulation parameters.degrees_of_constriction = default degrees of constriction wpi_set = [variables,parameters] return wpi_set } 77  Figure 5.1 (cont.) Algorithm used to optimize the WPIs Pseudocode climb_the_hill(wpi_set, step_size, LangA, LangB) { [variables,parameters] = wpi_set do { step_base_sep_factor = separation_factor(wpi_set, LangA, LangB) best_wpi_set = wpi_set best_step_sep_factor = 0 L = all neighboring variables For x in L { wpi_set = [x,parameters] sep_factor = separation_factor(wpi_set, LangA, LangB) if sep_factor > best_step_sep_factor { best_step_sep_factor = sep_factor best_wpi_set = wpi_set } } optimize_parameters(best_wpi_set, LangA, LangB) step_end_sep_factor = separation_factor(best_wpi_set, LangA, LangB) }while( step_end_sep_factor > step_base_sep_factor) } Pseudocode move_down_weights(wpi_set, step_size, LangA, LangB) { moved_down = true while (moved_down) { moved_down = false [variables,parameters] = wpi_set For each variable in variables { while (variable >= step_size) { variable = variable – step_size new_wpi_set = wpi_set with the new value of variable If (separation_factor(new_wpi_set, LangA, LangB) >= separation_factor(wpi_set, LangA, LangB)) { wpi_set = new_wpi_set moved_down = true } } } } } Pseudocode OptimizeWPIs(LangA, LangB) { while(number of already seen maxima < 5) { wpi_set = get_initial_WPIs_set() for step_size in [0.1, 0.01] { move_down_weights(wpi_set, step_size, LangA, LangB) climb_the_hill(wpi_set, step_size, LangA, LangB) #wpi_set generates a local maxima } } return wpi_set } 78  Figure 5.2 Illustration of the separation factor Separation b:1 Separation b:1 Separation b:1 Separation b:1 factor(x): l:0.9 factor(x): l:0.9 factor(x): l:0.9 factor(x): l:1 1.630 d:0.8 1.852 d:0.8 1.857 d:0.8 1.957 d:0.7 a:0.7 a:0.7 a:0.7 a:0.7 indel:2.1 e:0.6 indel:1.3 e:0.6 indel:1.3 e:0.6 indel:1.18 e:0.7 p:0.5 p:0.5 p:0.5 p:0.61 place:4.7 c:0.4 place:4.1 c:0.4 place:4.1 c:0.4 place:5.11 c:0.59 constriction:1.4 v:0.3 constriction:0 v:0.3 constriction:0 v:0.3 constriction:0.81 v:0.59 u:0.2 u:0.2 u:0.2 u:0.36 Places of articulation Places of articulation Places of articulation Places of articulation voice:4.2 voice:2.6 voice:2.6 voice:2.36 nasal:2.3 r:0.1 nasal:1.3 r:0.1 nasal:1.4 r:0.1 nasal:2.36 r:0.13 lateral:2.8 g:0 lateral:1.8 g:0 lateral:1.8 g:0 lateral:1.91 g:0 trill_flap:3.3 trill_flap:2.5 trill_flap:2.5 trill_flap:2.36 affricate:2.8 s:0 affricate:0.1 s:0 affricate:0 s:0 affricate:0 s:0 rounded:1 f:0.2 rounded:0 f:0.2 rounded:0 f:0.2 rounded:0.36 f:0.12 long_vowel:1.8 t:0.4 long_vowel:0.2 t:0.4 long_vowel:0.2 t:0.4 long_vowel:0.14 t:0.57 geminated:2.2 h:0.6 geminated:0.9 h:0.6 geminated:0.9 h:0.6 geminated:0.45 h:0.8 Degrees of Degrees of Degrees of Degrees of m:0.8 m:0.8 m:0.8 m:0.8 constrictions constrictions constrictions constrictions emphatic:2.9 emphatic:1.5 emphatic:1.6 emphatic:0.73 w:1 w:1 w:1 w:1 79  The optimal WPIs are calculated twice for each pair of varieties. The first time, the vowels are represented categorically based on the phonetic transcription. In this case long vowels are in three categories and short vowels in four; in the case of short vowels, there is schwa (Section 5.2). The second time, the vowels are represented based on the values derived from the formant frequencies reported in Section 2.8 (Section 5.3). 5.2 Measure of Pronunciation variation based on the mathematical representation of sound Following the algorithm presented in Figure 5.1, I calculate the WPIs for each pair of varieties. I ran the procedure twice for each pair of varieties to show the consistency of the algorithm in finding the optimal WPIs. Most values are very close to each other, if not exactly the same. The reliability of the algorithm could be enhanced by increasing the number of repeated local maxima required to find the optimal WPIs to a value bigger than five or by having a smaller step size. However, the achieved accuracy is considered satisfactory given the computational resources on hand. The optimal WPIs for all pairs of varieties are provided in Table 5.2. Then, the optimal WPIs for each pair of varieties are considered those that generated a bigger separation factor. See Section 6.2 and Section 6.3 for issues related to the values in this table. The amount of pronunciation variation is calculated based on the algorithm provided in Figure 4.3 with the cost of each basic operation in the Levenshtein distance algorithm calculated based on the optimal WPIs for the relevant pair of varieties. Table 5.3 summarizes the results. The closest varieties to each other are the closest geographically: LA, GA, and EA. MA is relatively more distant both geographically and based on the current measure of pronunciation variation. As with the previous measures, we still see the two patterns of asymmetry. First, the 80 amounts of variation between EA speakers and hearers of GA, LA, and MA are less than the amounts of variation between EA hearers and corresponding speakers from the those varieties. Second, the amounts of variation between LA hearers and speakers of EA, GA, and MA are less than the amounts of variation between LA speakers and hearers from the corresponding varieties. Table 5.4 reports the 95% confidence intervals for the amounts of pronunciation variation reported in Table 5.3. The highlighted rows show the intervals for the amounts of variation between MSA speakers and hearers from local varieties. As mentioned earlier it is more important to show the potential of the members of local varieties to comprehend MSA. GA hearers appear to be the closest to MSA speakers, followed by EA then LA. However, the 95% confidence intervals for those pairs of varieties overlap – the first three highlighted rows in Table 5.4. This means that we cannot confidently determine which of the three varieties is the closest to MSA from the measure of pronunciation variation based on the mathematical representation of sound. On the other hand, there is no overlap for the interval corresponding to MSA-MA with the other intervals for the formerly mentioned local varieties. So, the results of the current measure show that GA, EA, and LA are all closer to MSA than MA. The closest local variety to MA is EA considering both directions of communication – MA speakers to EA hearers and EA speakers to MA hearers. On the other hand, we cannot distinguish between the measures of closeness for LA and GA to MA due to the overlap of the relevant confidence intervals. Similar to the previous measure, EA speakers are closer to LA hearers than GA hearers are. Also, GA speakers are closer to LA hearers than EA hearers are. As for LA speakers, there is no distinction regarding the closeness of EA hearers and GA hearers to them. 81  Figure 5.3 summarizes the results for the lexical, pronunciation based on phone strings, and pronunciation based on mathematical representation methodologies. This plot is provided to make the comparison of the three variation metrics easier for the reader. It is not valid to compare the values from different variation metrics directly, but comparing the relative values within each metric is informative. Figure 5.3 Comparison of three variation metrics 0.6 0.5 0.4 0.3 Lexical 0.2 Phonemic 0.1 Features 0 GLF-EGY MOR-MSA MSA-EGY MSA-MOR GLF-MOR LEV-EGY EGY-MOR GLF-MSA LEV-MSA MOR-GLF EGY-MSA LEV-MOR MOR-LEV MSA-LEV EGY-GLF GLF-LEV MSA-GLF EGY-LEV LEV-GLF MOR-EGY 82  Table 5.2 WPIs calculated based on phonemic representations of vowels pair_name EA-GA EA-GA EA-LA EA-LA EA-MA EA-MA EA-SA EA-SA GA-LA GA-LA GA-MA GA-MA GA-SA GA-SA LA-MA LA-MA LA-SA LA-SA MA-SA MA-SA Trial number 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 separation_factor 2.077 2.026 2.227 2.212 1.830 1.829 2.298 2.266 1.966 1.960 1.651 1.648 2.324 2.322 1.779 1.775 2.049 2.044 1.772 1.772 p 0.574 0.559 0.530 0.525 0.600 0.601 0.721 0.667 0.641 0.641 0.817 0.819 0.652 0.645 0.915 1.011 0.715 0.710 0.897 0.888 cognates_mean 0.162 0.158 0.133 0.136 0.221 0.223 0.178 0.165 0.161 0.168 0.328 0.328 0.169 0.167 0.364 0.410 0.233 0.232 0.384 0.380 cognates_std 0.199 0.198 0.178 0.176 0.208 0.206 0.237 0.221 0.244 0.242 0.296 0.298 0.208 0.206 0.310 0.339 0.235 0.234 0.290 0.287 non_cognates_mean 0.972 0.959 0.882 0.875 0.923 0.925 1.275 1.165 1.166 1.154 1.277 1.275 1.258 1.240 1.367 1.491 1.177 1.161 1.400 1.386 non_cognates_std 0.192 0.197 0.158 0.158 0.176 0.177 0.241 0.220 0.267 0.262 0.279 0.277 0.261 0.256 0.254 0.270 0.225 0.221 0.284 0.281 numer of local maxima 70 50 21 41 28 47 50 39 70 93 45 48 88 61 51 89 38 39 23 26 place 5.03 4.99 2.43 2.53 2.03 1.73 5.29 4.56 5.71 5.11 6.67 12.68 7.65 7.56 4.51 4.17 5.73 4.86 10.96 10.8 constriction 1.99 2 2.03 0.93 0.69 1.33 3.82 2.42 1.54 0.81 1.19 0.25 2.44 1.99 4.31 2.37 1.92 1.77 0.88 0.88 voice 1.55 1.6 1.58 1.6 1.31 1.27 2.71 2.44 2.4 2.36 1.37 1.36 1.24 1.25 1.24 1.35 1.5 1.46 1.94 1.92 nasal 1.9 1.91 1.8 1.8 1.58 1.48 2.7 2.45 2.4 2.36 2.31 2.31 2.6 2.6 2.52 2.67 2.38 2.34 2.76 2.7 lateral 0 0.29 1.6 1.36 0.8 0.96 2.12 0.2 1.96 1.91 1.1 1.98 0.26 1.26 0.74 1.9 0 0 1.68 1.7 trill_flap 1.8 1.8 1.6 1.18 1.17 1.14 2.7 1.65 1.65 2.36 2.31 2.31 1.79 1.23 2.64 2.89 2.38 2.35 1.98 1.94 Weights africated 0 0 0 0 0.01 0.01 0 0 0 0 0 0 0 0.45 0 0 0 0 0 0 rounded 0.26 0.29 0 0 0.17 0.16 0 0 0.27 0.36 1 0.09 0.17 0.15 0 0.06 0 0 0 0 long_vowel 0.15 0.11 0.02 0.01 0 0 0 0 0.23 0.14 0.09 0.13 0.04 0.04 0.16 0.31 0.02 0.01 0.13 0.13 geminated 0.81 0.38 0.41 0.41 1.52 1.49 0.56 0.54 0.03 0.45 0 0 0 0 0 0 0 0 0 0 emphatic 0.59 0.58 0.33 0.12 1.58 1.58 0 0 0.83 0.73 1.25 1.26 2.6 1.87 1.18 2.39 0.69 0.71 1.06 1.05 indel 0.9 0.9 0.79 0.8 0.79 0.79 1.35 1.22 1.2 1.18 1.31 1.31 1.3 1.28 1.32 1.44 1.19 1.17 1.37 1.35 glottal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 pharyngeal 0.2 0.15 0.17 0.17 0 0 0.2 0.14 0.2 0.13 0.18 0.08 0.16 0.16 0.03 0 0.08 0.09 0.06 0.06 uvular 0.28 0.25 0.5 0.41 0.07 0.16 0.2 0.15 0.32 0.36 0.4 0.2 0.5 0.5 0.2 0.2 0.2 0.25 0.31 0.31 velar 0.69 0.7 0.5 0.5 0.4 0.5 0.5 0.4 0.5 0.59 0.4 0.2 0.5 0.5 0.2 0.2 0.2 0.25 0.31 0.31 central_vowel 0.7 0.7 0.5 0.5 0.4 0.5 0.5 0.4 0.5 0.59 0.41 0.28 0.5 0.5 0.41 0.56 0.2 0.26 0.38 0.38 palatal 0.7 0.7 0.5 0.5 0.4 0.5 0.5 0.4 0.51 0.61 0.47 0.31 0.52 0.53 0.47 0.64 0.2 0.27 0.43 0.43 postalveolar 0.7 0.7 0.5 0.5 0.4 0.5 0.5 0.5 0.7 0.7 0.7 0.7 0.7 0.56 0.6 0.7 0.7 0.7 0.7 0.7 alveolar 0.7 0.7 0.5 0.6 0.4 0.5 0.61 0.59 0.7 0.7 0.7 0.7 0.7 0.6 0.6 0.7 0.7 0.7 0.7 0.7 Places of articulation dental 0.7 0.7 0.5 0.6 0.8 0.8 0.61 0.59 0.7 0.7 0.7 0.7 0.7 0.6 0.6 0.7 0.7 0.7 0.7 0.7 labiodental 1 1 1 1 1 1 1 1 1 1 1 1 0.79 0.67 0.94 0.98 0.95 0.95 0.94 0.94 bilabial 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 stop 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 fricative 0 0.02 0.18 0.36 0.39 0.23 0.05 0.07 0.1 0.12 0.18 0.68 0.44 0.54 0.17 0.39 0.22 0.26 0 0 approximant 0.61 0.61 0.9 0.7 0.8 0.9 0.67 0.65 0.1 0.57 0.93 0.68 0.9 0.9 0.42 0.7 0.7 0.7 0.5 0.5 high_vowel 0.92 0.9 0.9 0.7 0.8 0.9 1 1 0.93 0.8 0.93 0.69 0.9 0.9 0.9 0.7 0.7 0.7 0.5 0.5 Degrees of constriction mid_vowel 0.92 0.9 0.9 0.7 0.8 0.9 1 1 0.93 0.8 0.93 0.69 0.91 0.9 0.9 0.77 0.71 0.7 0.5 0.5 low_vowel 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 83  Table 5.3 Results of the measure of pronunciation variation based on the mathematical representation of sound Hearer EA GA LA MA MSA EA 0.127 0.080 0.190 0.160 GA 0.161 0.101 0.309 0.161 Speaker LA 0.131 0.135 0.333 0.226 MA 0.208 0.291 0.295 0.362 MSA 0.154 0.134 0.174 0.335 Table 5.4 95% confidence intervals for the measure of pronunciation variation based on the mathematical representation of sound Speaker- Degrees of Mean of Range of 95% confidence interval Hearer freedom normalized distance EA-GA 206 0.127 0.105 - 0.15 EA-LA 226 0.08 0.064 - 0.096 EA-MA 176 0.19 0.162 - 0.217 EA-MSA 214 0.16 0.131 - 0.19 GA-EA 258 0.161 0.136 - 0.185 GA-LA 288 0.101 0.081 - 0.122 GA-MA 241 0.309 0.275 - 0.343 GA-MSA 282 0.161 0.138 - 0.185 LA-EA 308 0.131 0.112 - 0.15 LA-GA 314 0.135 0.112 - 0.158 LA-MA 266 0.333 0.299 - 0.366 LA-MSA 327 0.226 0.202 - 0.25 MA-EA 183 0.208 0.179 - 0.237 MA-GA 199 0.291 0.254 - 0.327 MA-LA 202 0.295 0.256 - 0.333 MA-MSA 204 0.362 0.322 - 0.401 MSA-EA 205 0.154 0.124 - 0.183 MSA-GA 218 0.134 0.109 - 0.16 MSA-LA 228 0.174 0.147 - 0.2 MSA-MA 188 0.335 0.298 - 0.373 84 5.3 Measure of Pronunciation variation based on the non-categorical representation of vowels In this section, the amount of pronunciation variation is determined based on the WPIs calculated following the procedure illustrated in Section 5.1 with the vowels represented by two numbers derived from the formant frequencies as described in Section 2.8. Figure 5.4 shows the distribution of coordinates of the vowels in the first two dimensions of the mathematical representation of sound. The circles show one standard deviation around the mean of the values of the two coordinates representing the vowel categories as calculated in Section 2.8. Solid circles correspond to long vowels and dashed circles correspond to short vowels. The place of articulation of the main articulatory gesture of the vowel is calculated as (velar + ((palatal- velar)*value derived from F2)). Similarly, the degree of constriction is calculated as (high_vowel + ((low_vowel – high_vowel)* value derived from the F1)). It is important to keep in mind that velar, palatal, high_vowel, and low_vowel correspond to parameters of the mathematical representation of sound as discussed in Section 5.1. Following the algorithm presented in Figure 5.1 with the new representation of vowels, I calculate the WPIs for each pair of varieties. Similar to the procedure in the previous section, I performed the calculation to find the optimal WPIs twice for each pair of varieties. The trials to find the optimal WPIs for all pairs of varieties are provided in Table 5.5. The trials were not carried out in the order given in the table: they are ordered to show the set of WPIs that generated the bigger separation factor and considered the optimal WPIs to show first in the table. The values of central_vowel and mid_vowel are omitted from Table 5.5 because the new representation is based on calculations that do not include midpoints. The amount of pronunciation variation was calculated based on the algorithm provided in Figure 4.3 with the cost of each basic operation in the Levenshtein distance algorithm based on the optimal WPIs for 85 the relevant pair of varieties including the non-categorical representation of vowels as discussed earlier. Table 5.6 summarizes the results. Unsurprisingly we see that GA, EA and LA are closer to each other, while MA seems more distant. The same pattern found with all linguistic variation metrics reported in this thesis: the geographically close varieties are also linguistically close. Table 5.7 reports the 95% confidence intervals for the amounts of pronunciation variation reported in Table 5.6. On a par with the findings of the previous measure, EA is closest to MA for both speakers and hearers. There is no distinction regarding the closeness of GA and LA to MA. There is also no distinction regarding the closeness of LA hearers and GA hearers to EA speakers. On the other hand, GA speakers are closer to LA hearers than EA hearers are. Moreover, LA speakers are closer to GA hearers than EA hearers are. Figure 5.4 Distribution of vowels indicating relevant places of articulation and degrees of constriction to factor the vowels into the mathematical representation of sound 86  Table 5.5 Summary of the results, based on formants pair_name EA-GA EA-GA EA-LA EA-LA EA-MA EA-MA GA-LA GA-LA GA-MA GA-MA LA-MA LA-MA Trial number 1 2 1 2 1 2 1 2 1 2 1 2 separation_factor 2.025 2.024 2.210 2.182 1.843 1.838 2.003 2.002 1.703 1.699 1.796 1.788 p 0.554 0.768 0.680 0.529 0.632 0.563 0.630 0.643 0.878 0.866 0.950 1.004 cognates_mean 0.163 0.233 0.181 0.136 0.233 0.202 0.165 0.163 0.350 0.345 0.384 0.410 cognates_std 0.193 0.265 0.226 0.180 0.217 0.196 0.232 0.240 0.310 0.306 0.315 0.333 non_cognates_mean 0.915 1.261 1.122 0.850 0.970 0.856 1.106 1.135 1.345 1.326 1.409 1.478 non_cognates_std 0.178 0.243 0.200 0.147 0.183 0.159 0.237 0.245 0.274 0.271 0.255 0.265 number of local maxima 168 183 110 39 28 36 122 63 61 45 49 38 place 3.33 4.96 4.14 2.82 1.99 1.96 5.44 5.78 6.67 6.67 4.78 5 constriction 1.07 1.23 1.96 1.03 1.1 0.62 0.3 1.11 1.1 1.05 2.77 1.31 voice 1.47 2.6 2.16 1.51 1.34 1.24 2.2 2.28 1.48 1.5 1.2 1.29 nasal 1.84 2.6 2.16 1.79 1.69 1.27 2.2 2.28 2.39 2.36 2.52 2.63 lateral 0.1 0.81 0.71 0.46 0.96 0.71 0.84 0.9 1.52 2.01 0.88 1.89 trill_flap 1.68 2.6 1.5 1.05 1.33 1.14 1.35 1.5 2.39 2.53 2.68 2.86 Weights africated 0 0 0 0 0 0.01 0 0 0 0 0 0 rounded 0.26 0.3 0 0 0.19 0.17 0.51 0.31 1 0.96 0.19 0.29 long_vowel 0.09 0.18 0 0.06 0 0 0.14 0.19 0.17 0.19 0.08 0.06 geminated 0.8 0.57 0.03 0.37 1.53 1.53 0 0 0 0 0.01 0 emphatic 0.4 0.39 0 0 1.69 1.44 0.89 0.87 1.3 1.22 2.26 2.43 indel 0.84 1.3 1.08 0.77 0.84 0.72 1.1 1.14 1.39 1.36 1.34 1.43 glottal 0 0 0 0 0 0 0 0 0 0 0 0 pharyngeal 0.14 0.11 0.16 0.18 0.01 0 0.17 0.15 0.19 0.2 0.03 0.01 uvular 0.18 0.2 0.4 0.36 0.12 0.08 0.31 0.3 0.4 0.4 0.2 0.2 velar 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.2 central_vowel - - - - - - - - - - - - palatal 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.41 0.4 0.48 0.54 postalveolar 0.5 0.5 0.4 0.4 0.4 0.4 0.67 0.67 0.7 0.7 0.6 0.7 alveolar 0.5 0.6 0.6 0.6 0.4 0.4 0.7 0.7 0.7 0.7 0.6 0.7 Places of articulation dental 0.5 0.6 0.6 0.6 0.8 0.8 0.7 0.7 0.7 0.7 0.6 0.7 labiodental 1 0.9 1 1 1 1 1 1 1 1 0.93 0.97 bilabial 1 1 1 1 1 1 1 1 1 1 1 1 stop 0 0 0 0 0 0 0 0 0 0 0 0 fricative 0.01 0.1 0.29 0.4 0.29 0.32 0.19 0.08 0.12 0.15 0.29 0.71 approximant 0.89 0.6 0.57 0.88 0.9 0.6 0.19 0.08 0.8 0.67 0.53 0.8 high_vowel 0.9 0.8 0.73 0.9 0.9 0.9 0.31 0.9 0.8 0.8 0.9 0.8 Degrees of constriction mid_vowel - - - - - - - - - - - - low_vowel 1 1 1 1 1 1 1 1 1 1 1 1 87  Table 5.6 Results of the measure of pronunciation variation based on the non-categorical representation of vowels Hearer EA GA LA MA EA 0.009 0.009 0.017 GA 0.011 0.007 0.024 Speaker LA 0.012 0.009 0.026 MA 0.018 0.023 0.024 (MSA excluded due to lack of acoustic data.) Table 5.7 95% confidence intervals for the measure of pronunciation variation based on the non-categorical representation of sound Speaker- Degrees of Mean of Range of 95% confidence interval Hearer freedom normalized distance EA-GA 377 0.009 0.008 - 0.011 EA-LA 414 0.009 0.008 - 0.011 EA-MA 323 0.017 0.015 - 0.019 GA-EA 356 0.011 0.01 - 0.013 GA-LA 396 0.007 0.006 - 0.008 GA-MA 332 0.024 0.021 - 0.026 LA-EA 406 0.012 0.01 - 0.013 LA-GA 412 0.009 0.007 - 0.01 LA-MA 349 0.026 0.024 - 0.028 MA-EA 322 0.018 0.016 - 0.02 MA-GA 348 0.023 0.021 - 0.025 MA-LA 353 0.024 0.021 - 0.026 88  CHAPTER 6 CONCLUSION In this thesis, I proposed a new methodology to computationally measure the amount of lexical and pronunciation variation between five varieties of Arabic. I argued for measuring the amount of linguistic variation asymmetrically. I used two tests of reliability with a convergence test and statistical tools. I also developed a new representation of sound for measuring pronunciation similarity that is mathematically based and computationally effective. This representation is able to represent sound categorically and non-categorically. Moreover, it has the ability to dynamically reflect the patterns of sound change based on pronunciation similarity of cognate words and pronunciation dissimilarity of non-cognate words. I incorporated the new representation of sound in two measures of pronunciation variation using the Levenshtein distance algorithm. I also implemented an optimization technique to set the costs of insertions, deletions, and substitutions of the Levenshtein distance algorithm, with the cost of substitution derived from the mathematical representation of sound. This allows the cost to be dynamically calculated based on the pronunciation similarity of the sounds being substituted. I developed two computational measures of lexical variation and three computational measures of pronunciation variation based on native speaker elicitations of the Swadesh list. The first computational measure of lexical variation was based on whether the hearer‟s variety has a cognate of the speaker variety‟s words for the same Swadesh list item. The second measure of lexical variation incorporated the pronunciation variation of the words by comparing their transcriptions in IPA. The first measure of pronunciation variation was phonemic where the costs of the basic operations of the Levenshtein distance algorithm were set to a default cost. The 89 second measure of pronunciation variation took into account phonetic similarity by incorporating the mathematical representation of sound in the calculation of the basic operations. The third measure of pronunciation variation used a non-categorical representation of vowels derived from the values of the first and second formant frequencies. All measures of linguistic variation developed in this thesis showed a consistent pattern where the geographically closer varieties tend to be also linguistically closer: EA, LA, and GA tend to be closer to each other than to MA. We also consistently found two patterns of asymmetry in the results of the lexical and pronunciation variation metrics. The asymmetry is indicated when the amount of variation between a speaker of a variety X and a hearer of a variety Y is not equal to the amount of variation between a speaker of a variety Y and a hearer of a variety X. The first pattern shows that the amounts of variation between EA speakers and hearers of GA, LA, and MA are less than the amounts of variation between EA hearers and speakers from the corresponding varieties. This reflects a pattern of mutual intelligibility we observe in the communication of Egyptians with members of other local varieties. The Egyptian speakers are understood better than they understand other speakers. This leads speakers of other varieties to accommodate Egyptian speakers in most cases. The second pattern of asymmetry shows that the amounts of variation between LA hearers and speakers of EA, GA, and MA are less than the amounts of variation between LA speakers and hearers from the corresponding varieties. This could imply that members of the LA variety are able to understand members of other varieties better than the other varieties understand them. The two claims regarding the patterns of asymmetry of the variation metrics for EA and LA speakers require verification by an independent study of mutual intelligibility. 90  The results of the first measure of lexical variation show that the closest variety to MSA is LA followed by both GA and EA. On the other hand, GA is measured to be the closest to MSA based on the other variation metrics that considered MSA. All variation metrics have indicated that MA is the farthest to MSA (see Section 6.1 for relevant discussion). The lexical and pronunciation variation metrics at the phonemic level resulted with LA second closest and EA third. The pronunciation variation metric at the phonetic level resulted with EA second and LA third. The first row in Table 6.1 shows the order of the closeness of the local varieties to MSA. The distinction in the measurement is considered not significant if there is an overlap in the 95% confidence intervals for the measurements. The non-significance in the difference of the closeness to MSA is indicated by grouping the varieties between braces or parentheses. For example, „{GA, (LA}, EA), MA‟ means that the closest to MSA is GA followed by LA, EA, then MA. However, there is an overlap in the confidence intervals for „{GA, LA}‟ and there is an overlap in the confidence intervals for „(LA, GA)‟. The second and third rows in Table 6.1 show the amount of variation between MA and the other local varieties. None of the variation metrics provided results that distinguish the closeness of GA and LA to MA. Therefore, GA and LA are grouped between braces. The second row indicates whether EA speakers are closer to MA hearers than GA and LA speakers to MA hearers. The third row indicates whether MA speakers are closer to EA hearers than MA speakers to GA and LA hearers. The lexical variation metric at the phonemic level did not provide any distinction regarding the closeness of the local varieties to MA. As can be seen in the second and third rows in Table 6.1, most pronunciation variation metrics developed in this research have indicated that EA is closer to MA than both GA and LA in both directions of communication. The fourth row shows that all variation metrics but the pronunciation variation 91 with non-categorical representation of vowels have indicated that EA speakers are closer to LA hearers than GA hearers are. The fifth row shows that, according to all variation metrics, GA speakers are closer to LA hearers than EA hearers are. Finally, only according to the pronunciation variation metric with the non-categorical representation of vowels, LA speakers are significantly closer to GA hearers than EA hearers are, as the sixth row shows. Table 6.1 Summary of the closeness of the Arabic varieties to each other Pronunciation with non-categorical Pronunciation at the phoneic level Pronunciation at the phonemic Lexical at the phonemic level vowel representation The comparison key Row number level 1 Order of closeness to MSA {GA, (LA}, EA), MA GA, {LA, EA}, MA {GA, EA, LA}, MA No MSA data 2 EA-MA < {GA, LA}-MA Not Significant YES YES YES 3 MA-EA < MA-{GA, LA} Not Significant Not Significant YES YES 4 EA-LA < EA-GA YES YES YES Not Significant 5 GA-LA < GA-EA YES YES YES YES 6 LA-GA < LA-EA Not Significant Not Significant Not Significant YES 92 6.1 The limited representation of the Arabic varieties As mentioned in Chapter 2, each local variety is represented by only two male native speakers from a major city where the variety under consideration is spoken. Other speakers from the same city or from other cities have different lexical inventories and different pronunciations to some degree. Moreover, the amount of variation is expected to show different patterns if rural areas are considered. The number of speakers and the geographical representation is considered a limitation of the study; these results would not necessarily generalize to other areas where the varieties are spoken. Also, the representation of MSA is derived from two modern dictionaries of Arabic, which does not necessarily capture all possible translations of the words of the Swadesh list. Moreover, the two modern dictionaries were authored by LA speakers, which raises the question of whether that biased MSA to be closer the LA to some degree? I selected those two dictionaries after careful consideration of the quality of their translations, with any bias expected to be marginal. However, it would still be worthwhile to see the effect on the results using dictionaries developed by speakers of other varieties. 6.2 Implications of different local maxima The optimization technique of the separation factor we followed produced a large number of maxima for each pair of varieties. The set of WPIs that generates the largest separation factor was selected as the optimal set of WPIs. However, other local maxima were not too remote from the selected optimal maximum – they also provided a meaningful representation of WPIs. Different local maxima can be seen as competing in identifying the right cost for different sets of combinations of sound changes. The large number of factors including the large number of pairs of varieties, the large number of pairs of words, and the large number of identified local maxima 93 makes it impossible to present all combinations of results in this thesis. I will focus on one pair of varieties and present a subset of the findings related to it; similar patterns are found for other pairings. The attempt to identify the optimal WPIs for the pair of varieties (LA and GA) resulted with 94 local maxima with the value of the separation factor ranging from 1.81 to 1.97, all of which are reasonable approximations. The total number of pairs of words included here is 1155, consisting of 398 non-cognate words and 757 cognate words. 19 out of the 757 pairs of cognate words were not identified correctly by any of the 94 local maxima. On the other hand, 663 pairs of cognate words were identified as cognates correctly by all local maxima. The focus of the following discussion is on a sample drawn from the 75 pairs of cognate words that were identified correctly as cognates by a subset of the local maxima. Table 6.2 contains a sample of cognate words divided into two categories based on the sound changes taking place in them. Category 1 shows a sample of the pairs of words that include an assimilation of ʔ to w or g. Pairs in this category were identified correctly by only 4 local maxima, but those local maxima failed to correctly identify cases of pairs reported in Table 6.2 under Category 2. In this case, the optimization technique failed to find a set of WPIs that could identify all words in Table 6.2; it could be the case that such set of WPIs does not exist. Table 6.2 Sample from LA-GA data set Levantine Gulf Category Number of local maxima able to identify ʔIʃ wIʃ 1 4 ʔAl gAl 1 4 tallaʒ θallaʤ 2 90 taliʒ θalʤ 2 65 94 6.3 Computational limitations The computational complexity of the algorithm used to find the optimal WPIs dictated some constraints to achieve a computationally feasible solution, and potential alternatives should be considered in the future. Increasing the scope from which the starting random values are selected could result in different optimal WPIs. Having a smaller step size to begin with could change the results as well. Increasing the number at which we stop the process of finding more local maxima might also result in a more accurate model. It is also possible to add more dimensions by specifying a more fine-grained cost of indels. The fundamental difference between insertions and deletions requires a thoughtful review of the matter. In deletions, the hearer is missing information that needs to be recovered, whereas in insertions the hearer is getting extra information that needs to be deleted. Consider for example the verbs rama and rma, meaning „threw‟ in LA and MA respectively. A speaker of MA produces the verb missing a vowel. In such a case, the LA hearer has to figure out the missing vowel and recover it. In the opposite direction of communication, a speaker of LA produces the verb with one extra vowel according to the MA. The assumption is that the MA hearer will have less difficulty deleting the extra information – the second vowel in this case – than the LA hearer who has to recover the missing information. This fundamental difference suggests a higher cost for deletions than insertions. In addition, inserting or deleting a vowel does not necessarily need to be equal to the cost of inserting or deleting a consonant, even though confidently setting a specific cost relative to the phonological operation (insertion or deletion) or category (consonant or vowel) may be open to debate. Assigning the same cost for both operations (insertion and deletion) has been the norm in previous research, as well as having a symmetric variation metric. However, in Heeringa and Braun (2003) the cost of insertion of a 95 vowel is assumed to be the cost of replacing that vowel with a schwa with the vowel feature set to 0. For consonants, they used a glottal stop with the consonant feature set to 0. From the perspective of the present study, this problem could be resolved by adding more dimensions to calculate more fine-grained cost of indels. For example, we could have four dimensions to evaluate – independent of each other – the costs of inserting a vowel, deleting a vowel, inserting a consonant, and deleting a consonant. Even a more fine-grained and computationally very expensive solution is to have a distinct dimension for inserting each phoneme and a distinct dimension for deleting each phoneme. Such solution is not feasible given the available computational resources. The mathematical representation of sound represents each phonetic feature and articulatory gesture by a distinct dimension in a multidimensional space. Geometrically speaking, each dimension is perpendicular on all other dimensions. This implies that the phonetic features and articulatory gestures are considered independent where a change in one of them does not carry any effect on the rest. This assumption is not motivated by linguistic theory; rather it is made for the sake of computational simplicity. Some features relate to others, such as rounding and backness in vowels. This imposes a limitation on the current study because these features are assumed to be independent, whereas a more accurate model would consider these interdependencies. Certainly, this is an area to be explored in further research. 6.4 Patterns of sound change and the mathematical representation of sound One of the most important factors of sound change is phonetic feature overlap or articulatory gesture overlap. An example of phonetic feature overlap is the assimilation of voicing when a voiceless fricative occurs between voiced segments. This is the case for example, 96 with the cognate words nisba and nizba „percentage‟ in LA and EA respectively. The voicing feature is introduced in the third phoneme of the Egyptian word because it occurs in the context of voiced phonetic segments. The two paths representing the words in the multidimensional representation are almost identical except for the third phoneme where there is a shift in the voicing dimension. Focusing on the dimension representing the voicing feature (Figure 6.1) we see that the path of the second word is always set to one value marking that all the phonemes are voiced, while in the first word the third phoneme is set to a voiceless value. Such sound change can be computationally approximated as smoothing the path connecting the points representing the phonemes, specifically in the voicing dimension. Some examples of sound change that can be accounted for as smoothing of the line connecting the pointes representing the phones in the mathematical representation of sound are the spread of the emphatic feature in the dialects of Arabic, vowel nasalization in context of nasals in American English, velarized nasals in the context of velars, and many other examples. It could be argued that such sound change when it is phonologically derived by features from neighboring segments should have a different penalty because it is considered as a natural sound change. This is certainly an important topic that deserves further investigations. Figure 6.1 The difference between nizba and nisba in the voicing dimension 1 Voicing nizba nisba 0 n i s-z b a 97 6.5 Suggestions for future research Based on the results of this research, I suggest that future research should continue to investigate new representations of acoustic segments. Mainly, representations that encode phonetic features and/or articulatory gestures. From an abstraction point of view, such representations are more detailed than the phonemic representation and more abstract – and less complex than – than the acoustic representation. I implemented a mathematical representation based on a hybrid model derived from articulatory phonology and autosegmental phonology. More significantly, I developed a model that ranks the fitness of the representation of sound based on its ability to reliably identify cognate words. This model can be used to investigate the fitness of other representations of sound based on other, or combinations of other, phonological theories. In addition, there is a room for improving the optimization technique by using more sophisticated computational methods. Because the focus of the current project is to measure the linguistic variation between a set of Arabic varieties, the model was kept as simple as possible to accomplish the task at hand. That said, the mathematical representation of sound could easily be enhanced to accommodate more complicated sound representations. The different degrees in which the phonetic features are manifested can be encoded in a multivalued scale in their corresponding dimension. For example, different types of phonation, such as creaky voicing, can be distinguished by providing more than one value in the voicing dimension. Another enhancement is to represent an utterance as a line in a multi-dimensional space. The model presented in this thesis represents utterances as sequence of points, whereas connecting the points in a way that reflects the transition of the phonetic features and articulatory gestures between the phonemes could result in a model that is more generally applicable. For the purposes of measuring the amount of variation, such a model 98 has consequences for the way distance is measured. Of course, here are consequences for each adaptation of the model, and these are left for future research. Another promising direction for extending this methodology would be to add an alignment function to the algorithm. This would be useful to identifying correspondences in the cognate words that would generalize to sound changes in the dialects. 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The remaining columns correspond to: - Speaker ID: each participant is given an ID that consists of 4 characters. The first two characters correspond to the variety abbreviation and the second two characters are a sequence number 01 or 02. - Word number: a sequence number of the translations provided for each lexical item by each participant. - Source of stimulus: an ID (ENG or VAR). ENG identifies the source of stimulus as the English word and context sentence given in the first pass. VAR identifies the source of stimulus as the word provided by other participants along with the English word and context sentence, elicited in the second pass. - Word in Arabic script: The transcription of the words in Arabic script according to the guidelines described in section 2.6. - Word in IPA script: The IPA transcription of the words. Long vowels are encoded by upper case letters and gemination feature for consonants is encoded by „+‟ sign. Word origin ID: a unique ID for each set of cognate words under each Swadesh list item. This ID is assigned to each elicited item based on the researcher‟s knowledge of the language. 105  Word in Arabic script Source of stimulus Word origin ID Word Number Word in IPA Swadesh item Speaker ID info ID: SWADESH_001 English word: I Context: ___ like the book EA01 1 ENG ‫َءٔا‬ ʔana 1 EA02 1 ENG ‫َءٔا‬ ʔana 1 GA01 1 ENG ‫َءٔا‬ ʔana 1 GA02 1 ENG ‫َءٔا‬ ʔana 1 LA01 1 ENG ‫َءٔا‬ ʔana 1 LA02 1 ENG ‫َءٔا‬ ʔana 1 MA01 1 ENG ‫َءٔا‬ ʔana 1 MA02 1 ENG ‫َءٔا‬ ʔana 1 SA01 1 ENG ‫َءٔا‬ ʔanA 1 ID: SWADESH_002 English word: you Context: (Talking to Ali.) ___ like the book EA01 1 ENG ‫ِءٔرا‬ ʔinta 1 EA02 1 ENG ‫ِءٔرا‬ ʔinta 1 GA01 1 ENG ‫َءٔرا‬ ʔanta 1 GA02 1 ENG ‫ِءٔرا‬ ʔinta 1 LA01 1 ENG ‫ِءٔرا‬ ʔinta 1 LA02 1 ENG ‫ٔرا‬ nta 1 MA01 1 ENG ‫ٔرا‬ nta 1 MA02 1 ENG ‫ٔرا‬ nta 1 SA01 1 ENG ََ‫َءٔد‬ ʔanta 1 ID: SWADESH_003 English word: he Context: ___ likes the book EA01 1 ENG َُٛ ٘ huw+a 1 EA02 1 ENG َُٛ٘ huw+a 1 106  GA01 1 ENG ‫ّا‬ُٛ٘ huw+a 1 GA02 1 ENG ٛ٘ hu 1 LA01 1 ENG َُٛ٘ huw+a 1 LA02 1 ENG َّْٛ ُ٘ huw+ə 1 MA01 1 ENG ‫ّا‬ُٛ٘ huw+a 1 MA02 1 ENG ‫ّا‬ُٛ٘ huw+a 1 SA01 1 ENG ََٛ ُ٘ huwa 1 ID: SWADESH_004 English word: we Context: ___ like the book EA01 1 ENG ‫ِءزٕا‬ ʔiħna 1 EA02 1 ENG ‫ِءزٕا‬ ʔiħna 1 GA01 1 ENG ‫زْ ّٕا‬ ħən+a 1 GA02 1 ENG ‫ِءزٕا‬ ʔiħna 1 LA01 1 ENG ‫ِءزٕا‬ ʔiħna 1 LA02 1 ENG ‫ِٔسٕا‬ niħna 1 MA01 1 ENG ‫زٕا‬ ħna 1 MA02 1 ENG ‫زٕا‬ ħna 1 SA01 1 ENG َُٓ‫َٔس‬ naħnu 1 ID: SWADESH_005 English word: you Context: (Talking to a group of 5 boys.) ___ like the book EA01 1 ENG ُُ‫ِءٔر‬ ʔintum 1 EA01 2 VAR ٛ‫ِءٔر‬ ʔintu 1 EA02 1 ENG ٛ‫ِءٔر‬ ʔintu 1 GA01 1 ENG ُُ‫َءٔر‬ ʔantum 1 GA02 1 ENG ْٛ‫ِءٔر‬ ʔintUn 1 LA01 1 ENG ٛ‫ِءٔر‬ ʔintu 1 LA02 1 ENG ٛ‫ءٔر‬ ʔntu 1 MA01 1 ENG ‫ِا‬ٛ‫ٔر‬ ntUma 1 MA02 1 ENG ‫ِا‬ٛ‫ٔر‬ ntUma 1 SA01 1 ENG ُُ‫َءٔر‬ ʔantum 1 ID: SWADESH_006 English word: they Context: (Talking about a group of 5 boys.) ___ like the book EA01 1 ENG ‫ُ٘ ّّا‬ hum+a 1 EA02 1 ENG ‫ُ٘ ّّا‬ hum+a 1 GA01 1 ENG ُُ٘ hum 1 GA02 1 ENG ُُ٘ hum 1 LA01 1 ENG ‫ُ٘ ّّا‬ hum+a 1 107  LA02 1 ENG َّْٓ ِ٘ hin+ə 1 LA02 2 ENG ّْٕٓ ِ٘ hin+ən 1 MA01 1 ENG ‫ِا‬ٛ٘ hUma 1 MA02 1 ENG ‫ِا‬ٛ٘ hUma 1 SA01 1 ENG ُُ٘ hum 1 ID: SWADESH_007 English word: this Context: (Pointing) ___ is a book EA01 1 ENG َ‫َد‬ da 1 EA02 1 ENG ‫ََد‬ da 1 GA01 1 ENG ‫٘ارا‬ hAða 1 GA02 1 ENG ‫ََد‬ da 1 LA01 1 ENG ‫٘اظ‬ hAðˤ 1 LA02 1 ENG ‫َ٘يذا‬ hayda 1 MA01 1 ENG ‫٘ادا‬ hAda 1 MA02 1 ENG ‫٘ادا‬ hAda 1 SA01 1 ENG ‫٘ارا‬ hAðA 1 ID: SWADESH_008 English word: that Context: (Pointing) ___ is a book EA01 1 ENG َ‫َد‬ da 1 EA02 1 ENG ‫ََد‬ da 1 GA01 1 ENG ‫٘اران‬ hAðAk 1 GA02 1 ENG ‫ََد‬ da 1 LA01 1 ENG ‫َ٘ظان‬ haðˤAk 1 LA02 1 ENG ‫َ٘يذان‬ haydAk 1 MA01 1 ENG ‫٘ادَن‬ hAdak 1 MA02 1 ENG ‫٘ادَن‬ hAdak 1 SA01 1 ENG ََ ‫را‬ ‫ن‬ ðAka 1 ID: SWADESH_009 English word: here Context: (Pointing) ___, on the table exists a book EA01 1 ENG ‫ِٕ٘ا‬ hina 1 EA02 1 ENG ‫ِٕ٘ا‬ hina 1 GA01 1 ENG ‫ِٕ٘ا‬ hina 1 GA02 1 ENG ٛ‫َء ّو‬ ʔak+u 2 LA01 1 ENG ْٛ٘ hUn 1 LA01 2 ENG ََْٛ٘ hUna 1 LA02 1 ENG ْٛ٘ hUn 1 MA01 1 ENG ‫ٕ٘ا‬ hna 1 108  MA02 1 ENG ‫ٕ٘ا‬ hna 1 SA01 1 ENG ‫ُٕ٘ا‬ hunA 1 ID: SWADESH_010 English word: there Context: (Pointing) ___, in the room exists a book EA01 1 ENG ‫ِٕ٘ان‬ hinAk 1 EA02 1 ENG ‫ِٕ٘ان‬ hinAk 1 GA01 1 ENG ‫ِٕ٘ان‬ hinAk 1 GA02 1 ENG ٛ‫َء ّو‬ ʔak+u 2 LA01 1 ENG ‫ُٕ٘ان‬ hunAk 1 LA02 1 ENG ‫ٔيه‬ٛ٘ hUnIk 1 MA01 1 ENG ‫ذَ ّّا‬ tam+a 3 MA01 2 VAR ‫ٕ٘ان‬ hnAk 1 MA02 1 ENG ‫ذَ ّّا‬ tam+a 3 MA02 2 ENG ٗ‫ي‬ٌٙ lhIh 4 SA01 1 ENG ََ‫ُٕ٘ان‬ hunAka 1 SA01 2 ENG ََُ‫ث‬ θam+a 3 ID: SWADESH_011 English word: who Context: ___ closed the door? EA01 1 ENG ٓ‫ِي‬ mIn 1 EA02 1 ENG ٓ‫ِي‬ mIn 1 GA01 1 ENG َِٓ man 1 GA02 1 ENG ِْٓ mən 1 LA01 1 ENG ٓ‫ِي‬ mIn 1 LA01 2 VAR َِٕٛ manU 1 LA02 1 ENG ٓ‫ِي‬ mIn 1 MA01 1 ENG ْٛ‫ؽى‬ ʃkUn 2 MA02 1 ENG ْٛ‫ؽى‬ ʃkUn 2 SA01 1 ENG َِٓ man 1 ID: SWADESH_012 English word: what Context: ___ did you eat yesterday? EA01 1 ENG ٗ‫ءي‬ ʔI 1 EA02 1 ENG ٗ‫ءي‬ ʔIh 1 GA01 1 ENG ‫ءيؼ‬ ʔIʃ 1 GA01 2 VAR ٕٛٙ‫ِؽ‬ ʃinhu 1 GA02 1 ENG ‫يؼ‬ٚ wIʃ 1 GA02 2 VAR ٕٛ‫ُؽ‬ ʃunu 1 LA01 1 ENG ٛ‫ؽ‬ ʃu 1 109  LA01 2 ENG ‫ءيؼ‬ ʔIʃ 1 LA02 1 ENG ٛ‫ؽ‬ ʃu 1 MA01 1 ENG ٕٛ‫ؽ‬ ʃnu 1 MA02 1 ENG ٕٛ‫ؽ‬ ʃnu 1 SA01 1 ENG ‫ِارا‬ mAðA 2 ID: SWADESH_013 English word: where Context: ___ did you go yesterday? EA01 1 ENG ٓ‫في‬ fIn 1 EA02 1 ENG ٓ‫في‬ fIn 1 GA01 1 ENG ٓ‫ي‬ٚ wIn 1 GA02 1 ENG ٓ‫ي‬ٚ wIn 1 LA01 1 ENG ٓ‫ي‬ٚ wIn 1 LA02 1 ENG ٓ‫ي‬ٚ wIn 1 MA01 1 ENG ٓ‫في‬ fIn 1 MA02 1 ENG ٓ‫في‬ fIn 1 SA01 1 ENG ََٓ‫َءي‬ ʔayna 1 ID: SWADESH_014 English word: when Context: ___ did you eat your breakfast? EA01 1 ENG ‫ِءِرا‬ ʔimta 1 EA02 1 ENG ‫ِءِرا‬ ʔimta 1 GA01 1 ENG ‫َِرا‬ mata 1 GA02 1 ENG ‫َِرا‬ mata 1 LA01 1 ENG ‫َِرا‬ mata 1 LA01 2 ENG ‫ِءِرا‬ ʔimta 1 LA02 1 ENG ‫ِءِرا‬ ʔImta 1 MA01 1 ENG ‫لاػ‬ٛ‫ف‬ fUqAʃ 2 MA02 1 ENG ‫ِءِرا‬ ʔimta 1 MA02 2 VAR ‫لراػ‬َٚ waqtAʃ 2 SA01 1 ENG ‫َِرا‬ matA 1 ID: SWADESH_015 English word: how Context: ___ did you repair the car? EA01 1 ENG ‫ِء ّصاي‬ ʔiz+Ay 1 EA02 1 ENG ‫ِء ّصاي‬ ʔiz+Ay 1 GA01 1 ENG ‫ويف‬ kIf 2 GA01 2 VAR ٍْٛ‫ؽ‬ ʃlUn 3 GA02 1 ENG ٍْٛ‫ؽ‬ ʃlUn 3 LA01 1 ENG ‫ويف‬ kIf 2 110  LA01 2 VAR ٍْٛ‫ؽ‬ ʃlUn 3 LA02 1 ENG ‫ويف‬ kIf 2 MA01 1 ENG ‫ويفاػ‬ kIfAʃ 2 MA02 1 ENG ‫ويفاػ‬ kIfAʃ 2 SA01 1 ENG ََ‫َويف‬ kayfa 2 ID: SWADESH_016 English word: not Context: Negating an adjective as in: The boy is ___ tall EA01 1 ENG ‫ِِؼ‬ miʃ 1 EA02 1 ENG ‫ِِؼ‬ miʃ 1 GA01 1 ENG ٛ٘‫ِا‬ mAhu 2 GA01 2 ENG ‫ُة‬ِْٙ məhub 2 GA01 3 VAR ‫ِْؼ‬ məʃ 1 GA02 1 ENG ِٛ mU 2 LA01 1 ENG ‫ِِؼ‬ miʃ 1 LA02 1 ENG َِّٕٛ man+u 2 LA02 2 VAR ‫ِِؼ‬ miʃ 1 MA01 1 ENG ‫ِاؽي‬ mAʃi 1 MA02 1 ENG ‫ِاؽي‬ mAʃi 1 SA01 1 ENG ََ ٌَ ‫يظ‬ laysa 3 ID: SWADESH_017 English word: all Context: ___ boys are tall EA01 1 ENG ً‫ُو‬ kul 1 EA02 1 ENG ً‫ُو‬ kul 1 GA01 1 ENG ً‫ِو‬ kil 1 GA02 1 ENG ً‫ِو‬ kil 1 LA01 1 ENG ً‫ُو‬ kul 1 LA02 1 ENG ً‫ِو‬ kil 1 MA01 1 ENG ‫گاع‬ gAʕ 2 MA02 1 ENG ‫گاع‬ gAʕ 2 SA01 1 ENG َّ ‫ُو‬ ً kul+ 1 ID: SWADESH_018 English word: many Context: ___ boys are tall EA01 1 ENG ‫ْوريش‬ kətIr 1 EA02 1 ENG ‫ْوريش‬ kətIr 1 GA01 1 ENG ‫وثيش‬ kθIr 1 GA01 2 VAR ‫ا ِخذ‬ٚ wAʤid 2 111  GA02 1 ENG ‫ا ِخذ‬ٚ wAʤid 2 LA01 1 ENG ‫وثيش‬ kθIr 1 LA02 1 ENG ‫وريش‬ ktIr 1 MA01 1 ENG ‫ْت ّضاف‬ bəz+Af 3 MA02 1 ENG ‫ْت ّضاف‬ bəz+Af 3 SA01 1 ENG ‫َوثيش‬ kaθIr 1 ID: SWADESH_019 English word: some Context: ___ boys are tall EA01 1 ENG ‫يح‬َٛ ‫ؽ‬ ʃwaya 1 EA02 1 ENG ‫يح‬َٛ ‫ْؽ‬ ʃəwaya 1 GA01 1 ENG ‫تَؼع‬ baʕðˤ 2 GA02 1 ENG ‫تَؼع‬ baʕðˤ 2 LA01 1 ENG ‫تَؼع‬ baʕðˤ 2 LA01 2 ENG ‫يح‬َٛ ‫ؽ‬ ʃway+ə 1 LA02 1 ENG ‫تَؼل‬ baʕdˤ 2 LA02 2 VAR ‫يِّح‬َٛ ‫ؽ‬ ʃway+ih 1 MA01 1 ENG ‫يح‬َٛ ‫ؽ‬ ʃway+a 1 MA01 2 VAR ‫تَؼل‬ baʕdˤ 2 MA02 1 ENG ‫ؽي‬ ʃi 1 MA02 2 VAR ‫تَؼل‬ baʕdˤ 2 SA01 1 ENG ‫تَؼل‬ baʕdˤ 2 ID: SWADESH_020 English word: few Context: ___ boys are tall EA01 1 ENG ُ‫َو‬ kam 1 EA01 2 VAR ‫تَؼل‬ baʕdˤ 2 EA01 3 VAR ‫يح‬َٛ ‫ْؽ‬ ʃəwaya 3 EA02 1 ENG ُ‫َو‬ kam 1 GA01 1 ENG ً‫ْگٍي‬ gəlIl 4 GA01 2 ENG ‫ي‬َٛ ‫ؽ‬ ʃway 3 GA02 1 ENG ‫تَؼع‬ baʕðˤ 2 LA01 1 ENG ‫يح‬َٛ ‫ؽ‬ ʃway+ə 3 LA01 2 ENG ً‫َگٍي‬ galIl 4 LA02 1 ENG ‫يِّح‬َٛ ‫ؽ‬ ʃway+ih 3 LA02 2 VAR ُ‫َو‬ kam 1 MA01 1 ENG ‫يح‬َٛ ‫ؽ‬ ʃway+a 3 MA02 1 ENG ً‫لٍي‬ qlIl 4 MA02 2 ENG ‫ؽي‬ ʃi 3 SA01 1 ENG ‫تَؼل‬ baʕdˤ 2 SA01 2 ENG ً‫لٍي‬ qlIl 4 112 ID: SWADESH_021 English word: other Context: I am not looking for this book,I am looking for the ___ book EA01 1 ENG ‫ذأي‬ tAni 1 EA02 1 ENG ‫ذأي‬ tAni 1 GA01 1 ENG ‫ثأي‬ θAni 1 GA02 1 ENG ‫ثأي‬ θAni 1 LA01 1 ENG ‫ثأي‬ θAni 1 LA02 1 ENG ‫ذأي‬ tAni 1 MA01 1 ENG ‫ءا ُخش‬ ʔAxur 2 MA02 1 ENG ‫ءا ُخش‬ ʔAxur 2 SA01 1 ENG ‫ءاخَش‬ ʔAxar 2 ID: SWADESH_022 English word: one Context: This is number ___ EA01 1 ENG ‫ا ِزذ‬ٚ wAħid 1 EA02 1 ENG ‫ازذ‬ٚ wAħd 1 GA01 1 ENG ‫ا ِزذ‬ٚ wAħid 1 GA02 1 ENG ‫ا ِزذ‬ٚ wAħid 1 LA01 1 ENG ‫ا َزذ‬ٚ wAħad 1 LA02 1 ENG ‫ا َزذ‬ٚ wAħad 1 MA01 1 ENG ‫ازْ ذ‬ٚ wAħəd 1 MA02 1 ENG ‫ازذ‬ٚ wAħd 1 SA01 1 ENG ‫ا ِزذ‬ٚ wAħid 1 ID: SWADESH_023 English word: two Context: This is number ___ EA01 1 ENG ٓ‫ءْذٕي‬ ʔətnIn 1 EA02 1 ENG ٓ‫ءْذٕي‬ ʔətnIn 1 GA01 1 ENG ٓ‫ثٕي‬ θnIn 1 GA02 1 ENG ٓ‫ءْثٕي‬ ʔəθnIn 1 LA01 1 ENG ٓ‫ثٕي‬ θnIn 1 LA02 1 ENG ٓ‫ذٕي‬ tnIn 1 MA01 1 ENG ‫ج‬ٛ‫خ‬ ʒUʒ 2 MA02 1 ENG ‫ج‬ٛ‫خ‬ ʒUʒ 2 SA01 1 ENG ْ‫ِءثٕا‬ ʔiθnAn 1 ID: SWADESH_024 English word: three Context: This is number ___ 113  EA01 1 ENG ‫ذَالذح‬ talAta 1 EA02 1 ENG ‫ذَالذح‬ talAta 1 GA01 1 ENG ‫ثَالثح‬ θalAθa 1 GA02 1 ENG ‫ثَالثح‬ θalAθa 1 LA01 1 ENG ‫ثَالز‬ θalAθ 1 LA02 1 ENG ‫ذالذح‬ tlAtə 1 MA01 1 ENG ‫ذالذح‬ tlAta 1 MA02 1 ENG ‫ذالذح‬ tlAta 1 SA01 1 ENG ‫ثَالثح‬ θalAθa 1 ID: SWADESH_025 English word: four Context: This is number ___ EA01 1 ENG ‫َءستَؼح‬ ʔarbaʕa 1 EA02 1 ENG ‫َءستَؼح‬ ʔarbaʕa 1 GA01 1 ENG ‫َءستؼح‬ ʔarbʕa 1 GA02 1 ENG ‫َءستَؼح‬ ʔarbaʕa 1 LA01 1 ENG ‫َءستؼح‬ ʔarbaʕa 1 LA02 1 ENG ‫َءستَ ََغ‬ ʔarbaʕa 1 LA02 2 VAR ََ ‫َء‬ ‫سب‬ ʔarba 1 MA01 1 ENG ‫سْ تؼا‬ rəbʕa 1 MA02 1 ENG ‫سْ تؼا‬ rəbʕa 1 SA01 1 ENG ‫َءستَؼح‬ ʔarbaʕa 1 ID: SWADESH_026 English word: five Context: This is number ___ EA01 1 ENG ‫خَّغح‬ xamsa 1 EA02 1 ENG ‫خَّغح‬ xamsa 1 GA01 1 ENG ‫خَّغح‬ xamsa 1 GA02 1 ENG ‫خَّغح‬ xamsa 1 LA01 1 ENG ‫خَّغح‬ xamsə 1 LA02 1 ENG ‫خَّغح‬ xamsə 1 MA01 1 ENG ‫ْخّغح‬ xəmsa 1 MA02 1 ENG ‫ْخّغح‬ xəmsa 1 SA01 1 ENG ‫خَّغح‬ xamsa 1 ID: SWADESH_027 English word: big Context: a ___ book EA01 1 ENG ‫وثيش‬ kbIr 1 EA02 1 ENG ‫ْوثيش‬ kəbIr 1 GA01 1 ENG ‫ْوثيش‬ kəbIr 1 114  GA02 1 ENG ‫َوثيش‬ kabIr 1 LA01 1 ENG ‫وثيش‬ kbIr 1 LA02 1 ENG ‫وثيش‬ kbIr 1 MA01 1 ENG ‫ْوثيش‬ kəbIr 1 MA02 1 ENG ‫وثيش‬ kbIr 1 SA01 1 ENG ‫َوثيش‬ kabIr 1 ID: SWADESH_028 English word: long Context: a ___ street EA01 1 ENG ً‫ي‬َٛ‫ه‬ tˤawIl 1 EA02 1 ENG ً‫ي‬َٛ‫ه‬ tˤawIl 1 GA01 1 ENG ً‫ي‬ُٛ‫ه‬ tˤuwIl 1 GA02 1 ENG ً‫ي‬َٛ‫ه‬ tˤawIl 1 LA01 1 ENG ً‫ي‬ٛ‫ه‬ tˤwIl 1 LA01 2 ENG ً‫ي‬َٛ‫ه‬ tˤawIl 1 LA02 1 ENG ً‫ي‬َٛ‫ه‬ tˤawIl 1 MA01 1 ENG ً‫ي‬ٛ‫ه‬ْ tˤəwIl 1 MA02 1 ENG ً‫ي‬ٛ‫ه‬ tˤwIl 1 SA01 1 ENG ً‫ي‬َٛ‫ه‬ tˤawyl 1 ID: SWADESH_029 English word: wide Context: a ___ street EA01 1 ENG ‫ا ِعغ‬ٚ wAsiʕ 1 EA02 1 ENG ‫ػَشيل‬ ʕarIdˤ 2 EA02 2 ENG ‫ا ِعغ‬ٚ wAsiʕ 1 GA01 1 ENG ‫عيغ‬ُٚ wusIʕ 1 GA02 1 ENG ‫ػَشيع‬ ʕarIðˤ 2 LA01 1 ENG ‫ا ِعغ‬ٚ wAsiʕ 1 LA01 2 VAR ‫ػَشيع‬ ʕarIðˤ 2 LA02 1 ENG ‫ػَشيل‬ ʕarIdˤ 2 LA02 2 VAR ‫ا ِعغ‬ٚ wAsiʕ 1 MA01 1 ENG ‫ا ِعغ‬ٚ wAsiʕ 1 MA02 1 ENG ‫ا ِعغ‬ٚ wAsiʕ 1 SA01 1 ENG ‫ا ِعغ‬ٚ wAsiʕ 1 SA01 2 ENG ‫ػَشيل‬ ʕarIdˤ 2 ID: SWADESH_030 English word: thick Context: a ___ wooden board EA01 1 ENG ٓ‫ْذخي‬ təxIn 1 EA02 1 ENG ٓ‫ْذخي‬ təxIn 1 115  EA02 2 VAR ‫َعّيه‬ samIk 2 GA01 1 ENG ‫عّْيه‬ səmIk 2 GA02 1 ENG ٓ‫َِري‬ matIn 3 LA01 1 ENG ٓ‫ثخي‬ θxIn 1 LA01 2 VAR ً‫خّي‬ xmIl 4 LA01 3 VAR ‫غٍَيع‬ ʁalIðˤ 5 LA02 1 ENG ‫عّيه‬ smIk 2 LA02 2 VAR ٓ‫هخي‬ tˤxIn 1 MA01 1 ENG ‫غٍيل‬ ʁlIdˤ 5 MA01 2 VAR ‫عّيه‬ smIk 2 MA02 1 ENG ‫غٍيل‬ ʁlIdˤ 5 SA01 1 ENG ‫َعّيه‬ samIk 2 SA01 2 ENG ٓ‫ثَخي‬ θaxIn 1 SA01 3 ENG ‫غٍَيع‬ ʁalIðˤ 5 ID: SWADESH_031 English word: heavy Context: a ___ wooden board EA01 1 ENG ً‫خءي‬ ْ təʔIl 1 EA02 1 ENG ً‫خءي‬ ْ təʔIl 1 GA01 1 ENG ً‫ْثگي‬ θəgIl 1 GA02 1 ENG ً‫ثَگي‬ θagIl 1 LA01 1 ENG ً‫ثگي‬ θgIl 1 LA02 1 ENG ً‫هءي‬ tˤʔIl 1 MA01 1 ENG ً‫ذمي‬ tqIl 1 MA02 1 ENG ً‫ذمي‬ tqIl 1 SA01 1 ENG ً‫ثَمي‬ θaqIl 1 ID: SWADESH_032 English word: small Context: a ___ wooden board EA01 1 ENG ‫فغَيش‬ ُ sˤuʁay+ar 1 EA02 1 ENG ‫فغَيش‬ ُ sˤuʁay+ar 1 GA01 1 ENG ‫فغيش‬ sˤʁIr 1 GA02 1 ENG ‫فغيش‬ َ sˤaʁIr 1 LA01 1 ENG ‫غيش‬،‫ص‬ zˤʁIr 1 LA02 1 ENG ‫غيش‬،‫ص‬ zˤʁIr 1 MA01 1 ENG ‫فغيش‬ sˤʁIr 1 MA02 1 ENG ‫فغيش‬ sˤʁIr 1 SA01 1 ENG ‫فغيش‬ َ sˤaʁIr 1 ID: SWADESH_033 English word: short 116 Context: a ___ man EA01 1 ENG ‫فيش‬ َ ‫ْء‬ ʔəsˤay+ar 1 EA02 1 ENG ‫فيش‬َ ‫ُء‬ ʔusˤay+ar 1 GA01 1 ENG ‫گقيش‬ gsˤIr 1 GA02 1 ENG ‫َگقيش‬ gasˤIr 1 LA01 1 ENG ‫وقيش‬ ksˤIr 1 LA01 2 ENG ‫َگقيش‬ gasˤIr 1 LA02 1 ENG ‫َءفيش‬ ʔasˤIr 1 MA01 1 ENG ‫لقيش‬ qsˤIr 1 MA02 1 ENG ‫لقيش‬ qsˤIr 1 SA01 1 ENG ‫لَقيش‬ qasˤIr 1 ID: SWADESH_034 English word: narrow Context: a ___ street EA01 1 ENG ‫دَيء‬ day+aʔ 1 EA02 1 ENG ‫دَيء‬ day+aʔ 1 GA01 1 ENG ‫ظَيِّگ‬ ðˤay+ig 1 GA02 1 ENG ‫ظَيِّگ‬ ðˤay+ig 1 LA01 1 ENG ‫ظَيِّگ‬ ðˤay+ig 1 LA02 1 ENG ‫م ِّيء‬ َ dˤay+iʔ 1 MA01 1 ENG ‫ميِّك‬ِ dˤiy+iq 1 MA02 1 ENG ‫ميِّك‬ ِ dˤiy+iq 1 SA01 1 ENG ‫ميِّك‬ َ dˤay+iq 1 ID: SWADESH_035 English word: thin Context: a ___ wooden board EA01 1 ENG ‫ُسفَيغ‬ rufay+aʕ 1 EA02 1 ENG ‫ُسفَيغ‬ rufay+aʕ 1 GA01 1 ENG ‫َعخيف‬ saxIf 2 GA01 2 VAR ‫ْٔسيف‬ nəħIf 3 GA02 1 ENG ‫ظَؼيف‬ ðˤaʕIf 4 LA01 1 ENG ‫ٔسيف‬ nħIf 3 LA01 2 VAR ‫َسگيگ‬ ragIg 5 LA02 1 ENG ‫َٔسيف‬ naħIf 3 LA02 2 VAR ‫سفيغ‬ rfIʕ 1 LA02 3 VAR ‫سءيء‬ rʔIʔ 5 MA01 1 ENG ‫سليك‬ rqIq 5 MA02 1 ENG ‫سليك‬ rqIq 5 SA01 1 ENG ‫َسفيغ‬ rafIʕ 1 SA01 2 ENG ‫َسليك‬ raqIq 5 SA01 3 ENG ‫َٔسيف‬ naħIf 3 117  SA01 4 ENG ‫مؼيف‬ َ dˤaʕIf 4 ID: SWADESH_036 English word: woman Context: This is a ___ EA01 1 ENG ‫ِعد‬ sit 1 EA02 1 ENG ‫ِعد‬ sit 1 GA01 1 ENG ‫زُشِح‬ ħurma 2 GA01 2 VAR ‫َِ ََش‬ mara 3 GA02 1 ENG ‫َِشا‬ mara 3 LA01 1 ENG ‫َِ ََش‬ mara 3 LA02 1 ENG ‫َِ ََش‬ mara 3 LA02 2 VAR ‫ِعد‬ sit 1 MA01 1 ENG ‫ِشا‬ mra 3 MA02 1 ENG ‫ِشا‬ mra 3 SA01 1 ENG ٖ‫ِءِ َش َء‬ ʔimraʔah 3 ID: SWADESH_037 English word: man (male) Context: This is a ___ EA01 1 ENG ً‫سا ِخ‬ rAgil 1 EA02 1 ENG ً‫سا ِخ‬ rAgil 1 GA01 1 ENG ‫َسخّاي‬ raʤ+Al 1 GA02 1 ENG ‫َسخّاي‬ raʤ+Al 1 LA01 1 ENG ‫َصٌَّح‬ zalamə 2 LA02 1 ENG ‫ِسخّاي‬ riʒ+Al 1 LA02 2 ENG ‫َصٌَّح‬ zalamə 2 MA01 1 ENG ً‫سا ِخ‬ rAʒil 1 MA02 1 ENG ً‫سا ِخ‬ rAʒil 1 SA01 1 ENG ًُ‫َسخ‬ raʤul 1 ID: SWADESH_038 English word: man (human) Context: This is a ___ (as opposed to other species) EA01 1 ENG ََ‫تَ ِٓءاد‬ baniʔAdam 1 EA02 1 ENG ََ‫تَ ِٓءاد‬ baniʔAdam 1 GA01 1 ENG ‫ءادِي‬ ʔAdmi 2 GA02 1 ENG ْ‫ِءٔغا‬ ʔinsAn 3 LA01 1 ENG ‫ءادِي‬ ʔAdmi 2 LA01 2 ENG ََ‫تَ ِٓءاد‬ baniʔAdam 1 LA02 1 ENG ْ‫ِءٔغا‬ ʔinsAn 3 MA01 1 ENG ََ‫تٕاد‬ bnAdam 1 MA01 2 ENG ْ‫ِءٔغا‬ ʔinsAn 3 118  MA02 1 ENG ََ‫تٕاد‬ bnAdam 1 SA01 1 ENG ْ‫ِءٔغا‬ ʔinsAn 3 SA01 2 ENG ‫ءادَِي‬ ʔAdamiy 2 SA01 3 ENG ََ‫ِءتٓءاد‬ ʔibnʔAdam 1 ID: SWADESH_039 English word: child Context: This is a ___ (5 years old) EA01 1 ENG ‫ٌَذ‬َٚ walad 1 EA01 2 VAR ًِّ‫َػي‬ ʕay+il 2 EA02 1 ENG ً‫ِهف‬ tˤifl 3 EA02 2 VAR ًِّ‫َػي‬ ʕay+il 2 GA01 1 ENG ً‫ه ْف‬ْ tˤəfəl 3 GA01 2 VAR ًِ٘ ‫خا‬ ʤAhil 4 GA02 1 ENG ًِ٘ ‫خا‬ ʤAhil 4 LA01 1 ENG ً‫ه ْف‬ ْ tˤəfəl 3 LA02 1 ENG ‫ٌَذ‬َٚ walad 1 LA02 2 VAR ً‫ه ْف‬ ْ tˤəfəl 3 MA01 1 ENG ‫ِدسّي‬ dir+i 5 MA01 2 VAR ‫ٌذ‬ِٚ wild 1 MA02 1 ENG ‫ٌذ‬ِٚ wild 1 MA02 2 VAR ‫ِدسّي‬ dir+i 5 SA01 1 ENG ً‫ِهف‬ tˤifl 3 ID: SWADESH_040 English word: wife Context: (as pronounced in the context provided) She is the ___ of Ali EA01 1 ENG ‫ِِشاخ‬ mirAt 1 EA01 2 VAR ‫خح‬ٚ‫ص‬ zUga 2 EA02 1 ENG ‫ِِشاخ‬ mirAt 1 GA01 1 ENG ‫ َخد‬ٚ‫ص‬ zUʤat 2 GA02 1 ENG ‫ َخد‬ٚ‫ص‬ zUʤat 2 LA01 1 ENG ‫َِ َشخ‬ marat 1 LA02 1 ENG ‫َِشخ‬ mart 1 LA02 2 VAR ‫ ِخد‬ٚ‫َص‬ zawʒit 2 MA01 1 ENG ‫ِشاخ‬ mrAt 1 MA02 1 ENG ‫ِشاخ‬ mrAt 1 SA01 1 ENG ‫ َخد‬ٚ‫َص‬ zawʤat 2 SA01 2 ENG ‫ِءِ َش َءخ‬ ʔimraʔat 1 ID: SWADESH_041 English word: husband Context: (as pronounced in the context provided) He is the ___ of Salma 119  EA01 1 ENG ‫ج‬ٚ‫ص‬ zUg 1 EA01 2 VAR ‫ص‬ٛ‫خ‬ gUz 1 EA02 1 ENG ‫ص‬ٛ‫خ‬ gUz 1 GA01 1 ENG ‫ج‬ٚ‫ص‬ zUʤ 1 GA01 2 VAR ً‫َس ِخ‬ raʤil 2 GA02 1 ENG ‫ج‬ٚ‫ص‬ zUʤ 1 GA02 2 VAR ً‫َس ِخ‬ raʤil 2 LA01 1 ENG ‫ص‬ٛ‫خ‬ ʤUz 1 LA02 1 ENG ‫ج‬ٚ‫ص‬ zUʒ 1 MA01 1 ENG ً‫سا ِخ‬ rAʒil 2 MA02 1 ENG ً‫سا ِخ‬ rAʒil 2 SA01 1 ENG ‫ج‬ٚ‫َص‬ zawʤ 1 ID: SWADESH_042 English word: mother Context: (as pronounced in the context provided) She is the ___ of Ali EA01 1 ENG ‫اٌ ِذخ‬ٚ wAldit 1 EA01 2 VAR َ‫ُء‬ ʔum 2 EA02 1 ENG َ‫ُء‬ ʔum 2 GA01 1 ENG َ‫ُء‬ ʔum 2 GA01 2 VAR ‫اٌذَخ‬ٚ wAldat 1 GA02 1 ENG َ‫ُء‬ ʔum 2 LA01 1 ENG َ‫ُء‬ ʔum 2 LA02 1 ENG َ‫ِء‬ ʔim 2 LA02 2 VAR ‫اٌ ِذخ‬ٚ wAldit 1 LA02 3 VAR ٖ‫اٌ ِذ‬ٚ wAldih 1 MA01 1 ENG ‫اٌِذا‬ٌٛ lwAlida 1 MA01 2 VAR ٓ‫ِّاي‬ m+Ayn 2 MA01 3 VAR ‫ِاِا‬ mAma 2 MA02 1 ENG ِّٛ m+u 2 MA02 2 ENG ‫ِاِا‬ mAma 2 SA01 1 ENG ‫اٌِذج‬ٚ wAlida 1 SA01 2 ENG َ‫ُء‬ ʔum 2 ID: SWADESH_043 English word: father Context: (as pronounced in the context provided) He is the ___ of Ali EA01 1 ENG ‫اٌِذ‬ٚ wAlid 1 EA01 2 VAR ٛ‫َءت‬ ʔabu 2 EA02 1 ENG ٛ‫َءت‬ ʔabu 2 GA01 1 ENG ٛ‫َءت‬ ʔabu 2 GA01 2 VAR ‫اٌِذ‬ٚ wAlid 1 GA02 1 ENG ٛ‫َءت‬ ʔabu 2 120  LA01 1 ENG ٛ‫َءت‬ ʔabu 2 LA02 1 ENG ٛ‫َءت‬ ʔabu 2 LA02 2 ENG ‫َءب‬ ʔab 2 LA02 3 VAR ‫اٌِذ‬ٚ wAlid 1 MA01 1 ENG ‫اٌِذ‬ٌٛ lwAlid 1 MA01 2 VAR ٓ‫تّاي‬ b+Ayn 2 MA01 3 VAR ‫تاتا‬ bAba 2 MA02 1 ENG ‫تّا‬ b+a 2 MA02 2 ENG ‫تاتا‬ bAba 2 SA01 1 ENG ‫َءب‬ ʔab 2 SA01 2 ENG ‫اٌِذ‬ٚ wAlid 1 ID: SWADESH_044 English word: animal Context: The elephant is a big ___ EA01 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 EA02 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 GA01 1 ENG ْ‫ا‬ٛ‫َزي‬ ħaywAn 1 GA02 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 LA01 1 ENG ْ‫ا‬ٛ‫َزي‬ ħaywAn 1 LA02 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 MA01 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 MA02 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 SA01 1 ENG ْ‫ا‬َٛ‫َزي‬ ħayawAn 1 ID: SWADESH_045 English word: fish Context: This ___ is about 20" in length, I am not sure what type it is. EA01 1 ENG ‫َع َّىح‬ samaka 1 EA02 1 ENG ‫َع َّىح‬ samaka 1 GA01 1 ENG ‫ع َّىح‬ smaka 1 GA02 1 ENG ‫َع َّچح‬ sama¢a 1 LA01 1 ENG ‫َع َّىح‬ samaka 1 LA02 1 ENG ‫َعّىح‬ samkə 1 MA01 1 ENG ‫ذح‬ٛ‫ز‬ ħUta 2 MA02 1 ENG ‫ذح‬ٛ‫ز‬ ħUta 2 SA01 1 ENG ‫َع َّىح‬ samaka 1 ID: SWADESH_046 English word: bird Context: This ___ is about 5" in length, I am not sure what type it is. EA01 1 ENG ‫س‬ٛ‫ػَقف‬ ʕasˤfUr 1 EA02 1 ENG ‫س‬ٛ‫ػَقف‬ ʕasˤfUr 1 121  GA01 1 ENG ‫س‬ٛ‫ػُقف‬ ʕusˤfUr 1 GA02 1 ENG ‫س‬ٛ‫ػُقف‬ ʕusˤfUr 1 LA01 1 ENG ‫س‬ٛ‫ػَقف‬ ʕasˤfUr 1 LA02 1 ENG ‫س‬ٛ‫ػَقف‬ ʕasˤfUr 1 MA01 1 ENG ‫هيش‬ tˤIr 2 MA02 1 ENG ‫هيش‬ tˤIr 2 SA01 1 ENG ‫س‬ٛ‫ػُقف‬ ʕusˤfUr 1 SA01 2 ENG ‫هَيش‬ tˤayr 2 ID: SWADESH_047 English word: dog Context: This is a ___ EA01 1 ENG ‫َوٍة‬ kalb 1 EA02 1 ENG ‫َوٍة‬ kalb 1 GA01 1 ENG ‫َوٍة‬ kalb 1 GA02 1 ENG ‫َچٍة‬ ¢alb 1 LA01 1 ENG ‫َوٍة‬ kalb 1 LA02 1 ENG ‫َوٍة‬ kalb 1 MA01 1 ENG ‫َوٍة‬ kalb 1 MA02 1 ENG ‫َوٍة‬ kalb 1 SA01 1 ENG ‫َوٍة‬ kalb 1 ID: SWADESH_048 English word: louse Context: The boy has one ___ in his hair EA01 1 ENG ‫َءٍِح‬ ʔamla 1 EA02 1 ENG ‫َءٍِح‬ ʔamla 1 GA01 1 ENG ‫َگٍّح‬ gamla 1 GA02 1 ENG ‫َگٍّح‬ gamla 1 LA01 1 ENG ‫َگٍّح‬ gamlə 1 LA02 1 ENG ‫َءٍِح‬ ʔamlə 1 MA01 1 ENG ‫ْگٍّح‬ gəmla 1 MA02 1 ENG ‫لٍَّح‬ qamla 1 SA01 1 ENG ‫لٍَّح‬ qamla 1 ID: SWADESH_049 English word: snake Context: A poisonous ___ is in the garden (the size of a walking stick) EA01 1 ENG ْ‫ْذؼثا‬ təʕbAn 1 EA01 2 VAR ‫َزيّح‬ ħay+a 2 EA02 1 ENG ْ‫ذؼثا‬ ْ təʕbAn 1 GA01 1 ENG ‫َزيّح‬ ħay+a 2 GA02 1 ENG ‫َزيّح‬ ħay+a 2 122  LA01 1 ENG ‫َزيّح‬ ħay+ə 2 LA02 1 ENG ‫َزيّح‬ ħay+ə 2 MA01 1 ENG ‫زْ ٕؼ‬ ħənʃ 3 MA02 1 ENG ‫زْ ٕؼ‬ ħənʃ 3 MA02 2 VAR ‫ٌفؼح‬ lfʕa 4 SA01 1 ENG ‫َزيّح‬ ħay+a 2 SA01 2 ENG ْ‫ثُؼثا‬ θuʕbAn 1 SA01 3 ENG ‫َءفؼا‬ ʔafʕA 4 ID: SWADESH_050 English word: worm Context: This is a ___ EA01 1 ENG ‫دج‬ٚ‫د‬ dUda 1 EA02 1 ENG ‫دج‬ٚ‫د‬ dUda 1 GA01 1 ENG ‫دج‬ٚ‫د‬ dUda 1 GA02 1 ENG ‫دج‬ٚ‫د‬ dUda 1 LA01 1 ENG ‫دج‬ٚ‫د‬ dUdə 1 LA02 1 ENG ‫دج‬ٚ‫د‬ dUdə 1 MA01 1 ENG ‫دج‬ٚ‫د‬ dUda 1 MA02 1 ENG ‫دج‬ٚ‫د‬ dUda 1 SA01 1 ENG ‫دج‬ٚ‫د‬ dUda 1 ID: SWADESH_051 English word: tree Context: This is a ___ EA01 1 ENG َ‫َؽ َد َش‬ ʃagara 1 EA02 1 ENG ‫َؽ َدشج‬ ʃagara 1 GA01 1 ENG ‫ؽ َدشج‬ ʃʤara 1 GA02 1 ENG ‫َؽ َدشج‬ ʃaʤara 1 LA01 1 ENG ‫َؽ َدشج‬ ʃaʤara 1 LA02 1 ENG ‫ؽَد ََش‬ ʃaʒra 1 MA01 1 ENG ‫ؽَدشج‬ ʃaʒra 1 MA02 1 ENG ‫ؽَدشج‬ ʃaʒra 1 SA01 1 ENG ‫َؽ َدشج‬ ʃaʤara 1 ID: SWADESH_052 English word: forest Context: This is a ___ EA01 1 ENG ‫غاتح‬ ʁAba 1 EA02 1 ENG ‫غاتح‬ ʁAba 1 GA01 1 ENG ‫غاتح‬ ʁAba 1 GA02 1 ENG ‫غاتح‬ ʁAba 1 LA01 1 ENG ‫غاتح‬ ʁAba 1 123  LA02 1 ENG ‫غاتح‬ ʁAbə 1 MA01 1 ENG ‫غاتح‬ ʁAba 1 MA02 1 ENG ‫غاتح‬ ʁAba 1 SA01 1 ENG ‫غاتح‬ ʁAba 1 ID: SWADESH_053 English word: stick Context: The boy is playing with a ___ (the size of a walking stick) EA01 1 ENG ‫ػَقايح‬ ʕasˤAya 1 EA02 1 ENG ‫ػَقايح‬ ʕasˤAya 1 GA01 1 ENG ٖ‫ػَقا‬ ʕasˤAh 1 GA02 1 ENG ‫ػَقايح‬ ʕasˤAya 1 LA01 1 ENG ‫ػَقايح‬ ʕasˤAyə 1 LA02 1 ENG ‫ػَقايح‬ ʕasˤAyə 1 MA01 1 ENG ‫ْػقا‬ ʕəsˤa 1 MA02 1 ENG ‫ْػقا‬ ʕəsˤa 1 MA02 2 ENG ‫د‬ٛ‫ػ‬ ʕUd 2 SA01 1 ENG ‫ػَقا‬ ʕasˤA 1 SA01 2 ENG ‫د‬ٛ‫ػ‬ ʕUd 2 ID: SWADESH_054 English word: fruit Context: This is a/an ___ (apple, orange, grape, strawberry, and banana) EA01 1 ENG ‫ح‬ٙ‫فاو‬ fAkha 1 EA02 1 ENG ‫ح‬ٙ‫فاو‬ fAkha 1 GA01 1 ENG ‫ح‬ٙ‫فاو‬ fAkha 1 GA02 1 ENG ٗ‫ا ِو‬َٛ‫ف‬ fawAkih 1 LA01 1 ENG ٗ‫ا ِو‬َٛ‫ف‬ fawAkih 1 LA02 1 ENG َ ٗ‫ا ِو‬ٛ‫ف‬ fawAkih 1 MA01 1 ENG ‫ح‬ٙ‫فا ِو‬ fAkiha 1 MA01 2 VAR ‫ديغيش‬ dIsIr 2 MA02 1 ENG ‫دي ِغش‬ dIsir 2 SA01 1 ENG ‫ح‬ٙ‫فا ِو‬ fAkiha 1 ID: SWADESH_055 English word: seed Context: I saw a watermelon that had only one ___ EA01 1 ENG ‫تِضسج‬ bizra 1 EA02 1 ENG ‫تِضسج‬ bizra 1 GA01 1 ENG ِٗ‫هؼا‬ ْ tˤəʕAmh 2 GA02 1 ENG ‫َزثّح‬ ħab+a 3 LA01 1 ENG ‫تِضسج‬ bizrə 1 LA02 1 ENG ‫تِضسج‬ bizrə 1 124  MA01 1 ENG ‫صسّيؼح‬ zr+Iʕa 4 MA02 1 ENG ‫صسّيؼح‬ zr+Iʕa 4 SA01 1 ENG ‫تِزسج‬ biðra 1 SA01 2 ENG ‫َزثّح‬ ħab+a 3 ID: SWADESH_056 English word: leaf Context: Only one ___ remains on the tree EA01 1 ENG ‫ َسءج‬َٚ waraʔa 1 EA02 1 ENG ‫ َسءج‬َٚ waraʔa 1 GA01 1 ENG ‫ َسلح‬َٚ waraga 1 GA02 1 ENG ‫ َسلح‬َٚ waraqa 1 LA01 1 ENG ‫ َسلح‬َٚ waraga 1 LA02 1 ENG ‫سءا‬َٚ warʔa 1 MA01 1 ENG ‫ْ سلح‬ٚ wərqa 1 MA02 1 ENG ‫ْ سلح‬ٚ wərqa 1 SA01 1 ENG ‫ َسلح‬َٚ waraqa 1 ID: SWADESH_057 English word: root Context: The tree ___ (is large) or (goes deep in the soil) EA01 1 ENG ‫ِخضس‬ gizr 1 EA02 1 ENG ‫ِخضس‬ gizr 1 GA01 1 ENG ‫َخ ِزس‬ ʤaðir 1 GA02 1 ENG ‫َخ ِذس‬ ʤadir 1 LA01 1 ENG ‫خْ ِذس‬ ʤəðir 1 LA01 2 VAR ‫ْؽشػ‬ ʃərʃ 2 LA02 1 ENG ‫ِخ ِضس‬ ʒizir 1 LA02 2 VAR ‫ِؽٍِؼ‬ ʃiliʃ 2 MA01 1 ENG ‫خ ِذس‬ ʒdir 1 MA02 1 ENG ‫خ ِذس‬ ʒdir 1 SA01 1 ENG ‫ِؽشػ‬ ʃirʃ 2 SA01 2 ENG ‫َخزس‬ ʤaðr 1 ID: SWADESH_058 English word: bark Context: The tree is surrounded by a layer of ___ to protect it EA01 1 ENG ‫ٌُساء‬ luħAʔ 1 EA02 1 ENG ‫ِءؽشج‬ ʔiʃra 2 GA01 1 ENG ‫گالفح‬ glAfa 3 GA02 1 ENG ‫ِگؾشج‬ giʃra 2 LA01 1 ENG ‫ٌْسا‬ ləħa 1 LA02 1 ENG ‫ِءؽشج‬ ʔiʃrə 2 125  MA01 1 ENG ‫ٌساف‬ lħAf 4 MA02 1 ENG ‫لؾشج‬ qʃra 2 MA02 2 ENG ‫غؾا‬ ʁʃa 5 SA01 1 ENG ‫ٌِساء‬ liħAʔ 1 ID: SWADESH_059 English word: flower Context: I don't know what kind of ___ this is EA01 1 ENG ‫سدج‬َٚ warda 1 EA01 2 VAR ‫صَ٘شج‬ zahra 2 EA02 1 ENG ‫سدج‬َٚ warda 1 GA01 1 ENG ‫سدج‬َٚ warda 1 GA02 1 ENG ‫صَ٘شج‬ zahra 2 GA02 2 ENG ‫سدج‬َٚ warda 1 LA01 1 ENG ‫سدج‬َٚ wardə 1 LA02 1 ENG ‫ص٘شا‬ zhra 2 MA01 1 ENG ‫ْ سدج‬ٚ wərda 1 MA02 1 ENG ‫ْ سدج‬ٚ wərda 1 SA01 1 ENG ‫صَ٘شج‬ zahra 2 ID: SWADESH_060 English word: grass Context: Wild ___ grows in forests EA01 1 ENG ‫َزؾيؼ‬ ħaʃIʃ 1 EA02 1 ENG ‫َزؾيؼ‬ ħaʃIʃ 1 GA01 1 ENG ‫ْػؾة‬ ʕəʃb 2 GA02 1 ENG ‫َزؾيؼ‬ ħaʃIʃ 1 LA01 1 ENG ‫ْػ ْؾة‬ ʕəʃəb 2 LA02 1 ENG ‫ِػ ِؾة‬ ʕiʃib 2 LA02 2 VAR ‫َزؾيؼ‬ ħaʃIʃ 1 MA01 1 ENG ‫ستيغ‬ rbIʕ 3 MA02 1 ENG ‫ستيغ‬ rbIʕ 3 SA01 1 ENG ‫ػُؾة‬ ʕuʃb 2 SA01 2 ENG ‫َزؾيؼ‬ ħaʃIʃ 1 ID: SWADESH_061 English word: rope Context: The tree climber uses a ___ EA01 1 ENG ً‫َزث‬ ħabl 1 EA02 1 ENG ً‫َزث‬ ħabl 1 GA01 1 ENG ًْ‫َزث‬ ħabəl 1 GA02 1 ENG ًْ‫َزث‬ ħabəl 1 LA01 1 ENG ًْ‫َزث‬ ħabəl 1 126  LA02 1 VAR ًْ‫َزث‬ ħabəl 1 MA01 1 ENG ‫سدا‬ٛ‫و‬ kUrda 2 MA01 2 VAR ًْ‫زث‬ ħbəl 1 MA01 3 VAR ‫ْل ّْٕثا‬ qən+əba 3 MA02 1 ENG ‫ْل ّْٕثا‬ qən+əba 3 MA02 2 VAR ًْ‫زث‬ ħbəl 1 MA02 3 VAR ‫سدا‬ٛ‫و‬ kUrda 2 SA01 1 ENG ً‫َزث‬ ħabl 1 ID: SWADESH_062 English word: skin Context: Africans mostly have darker ___ EA01 1 ENG ‫ِخٍذ‬ gild 1 EA01 2 VAR ‫تَؾشا‬ baʃra 2 EA02 1 ENG ‫ِخٍذ‬ gild 1 EA02 2 VAR ‫تَؾشا‬ baʃra 2 GA01 1 ENG ‫ِخٍذ‬ ʤild 1 GA01 2 VAR ‫تَؾشا‬ baʃra 2 GA02 1 ENG ‫ِخٍذ‬ ʤild 1 LA01 1 ENG ‫تَؾشا‬ baʃra 2 LA02 1 ENG ‫تَؾشا‬ baʃra 2 LA02 2 VAR ‫ِخٍِذ‬ ʒilid 1 MA01 1 ENG ‫خْ ٍذ‬ ʒəld 1 MA01 2 VAR ‫تؾشا‬ bʃra 2 MA02 1 ENG ‫خٍذا‬ ʒlda 1 MA02 2 ENG ‫تؾشا‬ bʃra 2 SA01 1 ENG ‫ِخٍذ‬ ʤild 1 SA01 2 ENG ‫تَؾَشج‬ baʃara 2 ID: SWADESH_063 English word: meat Context: ___ from beef is red EA01 1 ENG ُ‫ٌَس‬ laħm 1 EA02 1 ENG ُ‫ٌَس‬ laħm 1 GA01 1 ENG ُ ْ‫ٌَس‬ laħəm 1 GA02 1 ENG ُ ْ‫ٌَس‬ laħəm 1 LA01 1 ENG ُ ْ‫ٌَس‬ laħəm 1 LA02 1 ENG ُ ْ‫ٌَس‬ laħəm 1 MA01 1 ENG ُ ْ‫ٌس‬ lħəm 1 MA02 1 ENG ُ ْ‫ٌس‬ lħəm 1 SA01 1 ENG ُ‫ٌَس‬ laħm 1 127 ID: SWADESH_064 English word: blood Context: The ___ is red EA01 1 ENG ََ‫د‬ dam 1 EA02 1 ENG ََ‫د‬ dam 1 GA01 1 ENG ََ‫د‬ dam 1 GA02 1 ENG ََ‫د‬ dam 1 LA01 1 ENG ََ‫د‬ dam 1 LA02 1 ENG ََ‫د‬ dam 1 MA01 1 ENG َ‫ْد‬ dəm 1 MA02 1 ENG َ‫ْد‬ dəm 1 SA01 1 ENG ََ‫د‬ dam 1 ID: SWADESH_065 English word: bone Context: The ___ is white EA01 1 ENG ُ‫ػَن‬ ʕadˤm 1 EA02 1 ENG ُ‫ػَن‬ ʕadˤm 1 GA01 1 ENG ُ‫َظ‬ْ ‫ػ‬ ʕaðˤəm 1 GA02 1 ENG ُ‫َظ‬ ْ ‫ػ‬ ʕaðˤəm 1 LA01 1 ENG ُ‫َظ‬ ْ ‫ػ‬ ʕaðˤəm 1 LA02 1 ENG ُ ْ‫َػن‬ ʕadˤəm 1 MA01 1 ENG ُ ْ‫ػن‬ ʕdˤəm 1 MA02 1 ENG ُ‫ػظ‬ ْ ʕðˤəm 1 SA01 1 ENG ُ‫ػَظ‬ ʕaðˤm 1 ID: SWADESH_066 English word: fat Context: The ___ is white EA01 1 ENG ٓ‫عّْي‬ səmIn 1 EA02 1 ENG ٓ٘‫ِد‬ dihn 2 EA02 2 VAR ٓ‫عّْي‬ səmIn 1 GA01 1 ENG ‫ؽَسّح‬ ʃaħma 3 GA01 2 ENG ُ ْ‫ؽَس‬ ʃaħəm 3 GA02 1 ENG ُ ْ‫ؽَس‬ ʃaħəm 3 LA01 1 ENG ْٓ٘ ‫ْد‬ dəhən 2 LA02 1 ENG ْٓ٘ ‫ْد‬ dəhən 2 MA01 1 ENG ‫ؽسّح‬ ʃħma 3 MA02 1 ENG ‫ؽسّح‬ ʃħma 3 SA01 1 ENG ُٓ٘‫د‬ duhn 2 SA01 2 ENG ُ‫ؽَس‬ ʃaħm 3 128 ID: SWADESH_067 English word: egg Context: The chicken laid an ___ EA01 1 ENG ‫تينح‬ bIdˤa 1 EA02 1 ENG ‫تينح‬ bIdˤa 1 GA01 1 ENG ‫تيظح‬ bIðˤa 1 GA02 1 ENG ‫تيظح‬ bIðˤa 1 LA01 1 ENG ‫تيظح‬ bIðˤa 1 LA02 1 ENG ‫تَينا‬ baydˤa 1 MA01 1 ENG ‫تينح‬ bIdˤa 1 MA02 1 ENG ‫تينح‬ bIdˤa 1 SA01 1 ENG ‫تَينح‬ baydˤa 1 ID: SWADESH_068 English word: horn Context: The bull has a broken ___ EA01 1 ENG ْ‫َءس‬ ʔarn 1 EA02 1 ENG ْ‫َءس‬ ʔarn 1 GA01 1 ENG ْ‫َگش‬ garn 1 GA02 1 ENG ْ‫َگش‬ garn 1 LA01 1 ENG ْ ْ‫ْگش‬ gərən 1 LA02 1 ENG ْ ْ‫ْءس‬ ʔərən 1 MA01 1 ENG ْ‫ْگش‬ gərn 1 MA02 1 ENG ْ‫ْگش‬ gərn 1 SA01 1 ENG ْ‫لَش‬ qarn 1 ID: SWADESH_069 English word: tail Context: The fox has a nice ___ EA01 1 ENG ً‫دي‬ dIl 1 EA02 1 ENG ً‫دي‬ dIl 1 GA01 1 ENG ً‫ري‬ ðIl 1 GA02 1 ENG ‫َدَٔة‬ danab 2 GA02 2 VAR ً‫دي‬ dIl 1 LA01 1 ENG ً‫ري‬ ðIl 1 LA02 1 ENG ‫َدَٔة‬ danab 2 MA01 1 ENG ‫ْصٔطيو‬ zəntˤItˤ 3 MA01 2 VAR ً‫دي‬ dIl 1 MA02 1 ENG ً‫دي‬ dIl 1 MA02 2 ENG ‫اي‬ٛ‫ؽ‬ ʃwAl 4 MA02 3 VAR ‫ْصٔطيو‬ zəntˤItˤ 3 SA01 1 ENG ً‫َري‬ ðayl 1 SA01 2 ENG ‫َرَٔة‬ ðanab 2 129 ID: SWADESH_070 English word: feather Context: The bird dropped a nice ___ EA01 1 ENG ‫سيؾح‬ rIʃa 1 EA02 1 ENG ‫سيؾح‬ rIʃa 1 GA01 1 ENG ‫سيؾح‬ rIʃa 1 GA02 1 ENG ‫سيؾح‬ rIʃa 1 LA01 1 ENG ‫سيؾح‬ rIʃə 1 LA02 1 ENG ‫سيؾح‬ rIʃə 1 MA01 1 ENG ‫سيؾح‬ rIʃa 1 MA02 1 ENG ‫سيؾح‬ rIʃa 1 SA01 1 ENG ‫سيؾح‬ rIʃa 1 ID: SWADESH_071 English word: hair Context: Human ___ comes in different colors (singlular) EA01 1 ENG ‫ؽَؼشج‬ ʃaʕra 1 EA02 1 ENG ‫ؽَؼشج‬ ʃaʕra 1 GA01 1 ENG ‫ؽَؼشج‬ ʃaʕra 1 GA02 1 ENG ‫ؽَؼشج‬ ʃaʕra 1 LA01 1 ENG ‫ؽَؼشا‬ ʃaʕra 1 LA02 1 ENG ‫ؽَؼشا‬ ʃaʕra 1 MA01 1 ENG ‫ْؽؼشج‬ ʃəʕra 1 MA02 1 ENG ‫ْصغثح‬ zəʁba 2 MA02 2 ENG ‫ْؽؼشج‬ ʃəʕra 1 SA01 1 ENG ‫ؽَؼشج‬ ʃaʕra 1 ID: SWADESH_072 English word: head Context: The cheetah's ___ is small EA01 1 ENG ‫ساط‬ rAs 1 EA01 2 ENG ‫ِدِاؽ‬ dimAʁ 2 EA02 1 ENG ‫ساط‬ rAs 1 EA02 2 VAR ‫ِدِاؽ‬ dimAʁ 2 GA01 1 ENG ‫ساط‬ rAs 1 GA02 1 ENG ‫ساط‬ rAs 1 LA01 1 ENG ‫ساط‬ rAs 1 LA02 1 ENG ‫ساط‬ rAs 1 MA01 1 ENG ‫ساط‬ rAs 1 MA02 1 ENG ‫ساط‬ rAs 1 SA01 1 ENG ‫َسءط‬ raʔs 1 130 ID: SWADESH_073 English word: ear Context: The boy has an ___ EA01 1 ENG ْ‫د‬ِٚ widn 1 EA02 1 ENG ْ‫د‬ِٚ widn 1 GA01 1 ENG ْ‫ء ْْر‬ ʔəðən 1 GA02 1 ENG ْٚ‫َءد‬ ʔadUn 1 LA01 1 ENG ْ‫ِء ْر‬ ʔiðən 1 LA02 1 ENG ‫دَيٕح‬ daynə 1 MA01 1 ENG ْ‫ْ ِد‬ٚ wədin 1 MA02 1 ENG ْ‫ْ ِد‬ٚ wədin 1 SA01 1 ENG ْ‫ُء ُر‬ ʔuðun 1 ID: SWADESH_074 English word: eye Context: The boy has an ___ EA01 1 ENG ٓ‫ػي‬ ʕIn 1 EA02 1 ENG ٓ‫ػي‬ ʕIn 1 GA01 1 ENG ٓ‫ػي‬ ʕIn 1 GA02 1 ENG ْٛ‫ػي‬ ʕyUn 1 LA01 1 ENG ٓ‫ػي‬ ʕIn 1 LA02 1 ENG ٓ‫ػي‬ ʕIn 1 MA01 1 ENG ٓ‫ػي‬ ʕIn 1 MA02 1 ENG ٓ‫ػي‬ ʕIn 1 SA01 1 ENG ٓ‫ػَي‬ ʕayn 1 ID: SWADESH_075 English word: nose Context: The boy has a ___ EA01 1 ENG ‫َِٕاخيش‬ manAxIr 1 EA02 1 ENG ‫َِٕاخيش‬ manAxIr 1 GA01 1 ENG ُ‫َخ ِؾ‬ xaʃim 2 GA02 1 ENG ُ‫َخ ِؾ‬ xaʃim 2 LA01 1 ENG ُ‫ْخ ْؾ‬ xəʃəm 2 LA02 1 ENG ‫ِْٕخاس‬ mənxAr 1 LA02 2 ENG ‫َءٔف‬ ʔanf 3 MA01 1 ENG ‫ٔيف‬ nIf 3 MA01 2 VAR ‫ِْٕخاس‬ mənxAr 1 MA02 1 ENG ‫ٔيف‬ nIf 3 MA02 2 VAR ‫ِْٕخاس‬ mənxAr 1 SA01 1 ENG ‫َءٔف‬ ʔanf 3 SA01 2 ENG ‫ُِٕخَش‬ munxar 1 131 ID: SWADESH_076 English word: mouth Context: The boy has a ___ EA01 1 ENG ‫بُ ء‬ buʔ 1 EA02 1 ENG ‫بُ ء‬ buʔ 1 GA01 1 ENG َُ‫ف‬ fam 2 GA02 1 ENG ‫ص‬ٛ‫ت‬ bUz 3 LA01 1 ENG ُ‫ْث‬ θəm 4 LA02 1 ENG ُ‫ْذ‬ təm 4 MA01 1 ENG ُُ‫ف‬ fum 2 MA02 1 ENG ُُ‫ف‬ fum 2 SA01 1 ENG َُ‫ف‬ fam 2 ID: SWADESH_077 English word: tooth Context: The boy has a ___ (referring to the incisors) EA01 1 ENG ‫ِعّٕح‬ sin+a 1 EA02 1 ENG ‫ِعّٕح‬ sin+a 1 GA01 1 ENG ٓ‫ِع‬ sin 1 GA01 2 VAR ‫ِظشط‬ ðˤirs 2 GA02 1 ENG ‫مشط‬ َ dˤars 2 LA01 1 ENG ٓ‫ِع‬ sin 1 LA02 1 ENG ٓ‫ِع‬ sin 1 MA01 1 ENG ‫عّٕح‬ sn+a 1 MA02 1 ENG ‫عّٕح‬ sn+a 1 SA01 1 ENG َّ ‫ِع‬ ٓ sin+ 1 SA01 2 ENG ‫مشط‬ ِ dˤirs 2 ID: SWADESH_078 English word: tongue Context: The boy has a ___ EA01 1 ENG ْ‫ٌِغا‬ lisAn 1 EA02 1 ENG ْ‫ٌِغا‬ lisAn 1 GA01 1 ENG ْ‫ٌغا‬ lsAn 1 GA02 1 ENG ْ‫ٌغا‬ lsAn 1 LA01 1 ENG ْ‫ٌغا‬ lsAn 1 LA02 1 ENG ْ‫ٌغا‬ lsAn 1 MA01 1 ENG ْ‫ٌغا‬ lsAn 1 MA02 1 ENG ْ‫ٌغا‬ lsAn 1 SA01 1 ENG ْ‫ٌِغا‬ lisAn 1 132 ID: SWADESH_079 English word: fingernail Context: The boy has a ___ EA01 1 ENG ‫مفش‬ ِ dˤifr 1 EA02 1 ENG ‫مْ فش‬ dˤəfr 1 GA01 1 ENG ‫ظُ ْفش‬ ðˤufər 1 GA02 1 ENG ‫ظُ ْفش‬ ðˤufər 1 LA01 1 ENG ‫ِءظفَش‬ ʔiðˤfar 1 LA02 1 ENG ‫مْ ْفش‬ dˤəfər 1 MA01 1 ENG ‫د ْفش‬ dfər 1 MA02 1 ENG ‫د ْفش‬ dfər 1 SA01 1 ENG ‫ظُفش‬ ðˤufr 1 ID: SWADESH_080 English word: foot Context: The boy has a ___ EA01 1 ENG ً‫ِسخ‬ rigl 1 EA02 1 ENG ً‫ِسخ‬ rigl 1 GA01 1 ENG ً‫ِس ِخ‬ riʤil 1 GA02 1 ENG ً‫ِس ِخ‬ riʤil 1 LA01 1 ENG ً‫ِس ِخ‬ riʤil 1 LA01 2 VAR ‫ِء ِخش‬ ʔiʒir 2 LA02 1 ENG ‫ِءخْ ش‬ ʔiʒər 2 MA01 1 ENG ً ْ‫سخ‬ rʒəl 1 MA02 1 ENG ً ْ‫سخ‬ rʒəl 1 SA01 1 ENG ََ‫لَذ‬ qadam 3 ID: SWADESH_081 English word: leg Context: The boy has a ___ EA01 1 ENG ً‫ِسخ‬ rigl 1 EA02 1 ENG ً‫ِسخ‬ rigl 1 GA01 1 ENG ً‫ِس ِخ‬ riʤil 1 GA02 1 ENG ً‫ِس ِخ‬ riʤil 1 LA01 1 ENG ً‫ِس ِخ‬ riʤil 1 LA01 2 VAR ‫ِء ِخش‬ ʔiʒir 2 LA02 1 ENG ‫ِءخْ ش‬ ʔiʒər 2 MA01 1 ENG ً ْ‫سخ‬ rʒəl 1 MA02 1 ENG ً ْ‫سخ‬ rʒəl 1 133  SA01 1 ENG ‫عاق‬ sAq 3 SA01 2 ENG ً‫ِسخ‬ riʤl 1 ID: SWADESH_082 English word: knee Context: The boy has a ___ EA01 1 ENG ‫سُوثح‬ rukba 1 EA02 1 ENG ‫سُوثح‬ rukba 1 GA01 1 ENG ‫سْ وثح‬ rəkba 1 GA02 1 ENG ‫ِسوثح‬ rikba 1 LA01 1 ENG ‫سُوثح‬ rukba 1 LA02 1 ENG ‫ِسوثح‬ rikbə 1 MA01 1 ENG ‫سْ وثح‬ rəkba 1 MA02 1 ENG ‫سْ وثح‬ rəkba 1 SA01 1 ENG ‫سُوثح‬ rukba 1 ID: SWADESH_083 English word: hand Context: The boy has a ___ EA01 1 ENG ‫ءيذ‬ ʔId 1 EA02 1 ENG ‫ءيذ‬ ʔId 1 GA01 1 ENG ‫يَذ‬ yad 1 GA02 1 ENG ‫ءيذ‬ ʔId 1 LA01 1 ENG ‫ءيذ‬ ʔId 1 LA02 1 ENG ‫ءيذ‬ ʔId 1 MA01 1 ENG ‫يْذ‬ yəd 1 MA02 1 ENG ‫يْذ‬ yəd 1 SA01 1 ENG ‫يَذ‬ yad 1 ID: SWADESH_084 English word: wing Context: The bird has a ___ EA01 1 ENG ‫ِخٕاذ‬ ginAħ 1 EA02 1 ENG ‫ِخٕاذ‬ ginAħ 1 GA01 1 ENG ‫َخٕاذ‬ ʤanAħ 1 GA02 1 ENG ‫َخٕاذ‬ ʤanAħ 1 LA01 1 ENG ‫خٕاذ‬ ʒnAħ 1 LA02 1 ENG ‫خٕاذ‬ ʒnAħ 1 MA01 1 ENG ‫خٕاذ‬ ʒnAħ 1 MA02 1 ENG ‫خٕاذ‬ ʒnAħ 1 134  SA01 1 ENG ‫َخٕاذ‬ ʤanAħ 1 ID: SWADESH_085 English word: belly Context: The boy has a ___ (the boy is slim) EA01 1 ENG ٓ‫تَط‬ batˤn 1 EA02 1 ENG ٓ‫تَط‬ batˤn 1 GA01 1 ENG ْ َ‫ت‬ ٓ‫ط‬ batˤən 1 GA02 1 ENG ّٗ‫َدت‬ dab+ah 2 GA02 2 ENG ٓ‫ط‬ْ َ‫ت‬ batˤən 1 LA01 1 ENG ٓ‫ط‬ ْ َ‫ت‬ batˤən 1 LA02 1 ENG ٓ‫ط‬ ْ َ‫ت‬ batˤən 1 MA01 1 ENG ‫ْوشػ‬ kərʃ 3 MA02 1 ENG ‫ْوشػ‬ kərʃ 3 SA01 1 ENG ٓ‫تَط‬ batˤn 1 ID: SWADESH_086 English word: guts Context: The ___ of a cow are big (Everything in the abdomen) EA01 1 ENG ‫َءزؾاء‬ ʔaħʃAʔ 1 EA02 1 ENG ٓ‫َِقاسي‬ masˤArIn 2 GA01 1 ENG ‫َءِؼاء‬ ʔamʕAʔ 3 GA01 2 VAR ٓ‫َِقاسي‬ masˤArIn 2 GA02 1 ENG ٓ‫َِقاسي‬ masˤArIn 2 LA01 1 ENG ٓ‫قشي‬ َ َِ masˤarIn 2 LA02 1 ENG ٓ‫ِقاسي‬ msˤArIn 2 MA01 1 ENG ْ‫ِقاس‬ ِ msˤArin 2 MA02 1 ENG ْ‫ِقاس‬ ِ msˤArin 2 SA01 1 ENG ‫َءزؾاء‬ ʔaħʃAʔ 1 ID: SWADESH_087 English word: neck Context: The boy has a ___ EA01 1 ENG ‫َس َءتح‬ raʔaba 1 EA02 1 ENG ‫َس َءتح‬ raʔaba 1 GA01 1 ENG ‫س ُگثح‬ rguba 1 GA02 1 ENG ‫َس َگثح‬ ragaba 1 LA01 1 ENG ‫َس َگثح‬ ragaba 1 LA02 1 ENG ‫َسءتح‬ raʔbə 1 MA01 1 ENG ‫ْػٕك‬ ʕənq 2 MA02 1 ENG ‫ػَٕك‬ ʕanq 2 SA01 1 ENG ‫َسلَثح‬ raqaba 1 135  SA01 2 ENG ‫ػُٕك‬ ʕunq 2 ID: SWADESH_088 English word: back Context: The boy has a ___ EA01 1 ENG ‫ش‬ٙ‫م‬ َ dˤahr 1 EA02 1 ENG ‫ش‬ٙ‫م‬ َ dˤahr 1 GA01 1 ENG ‫َش‬َٙ‫ظ‬ ðˤahar 1 GA02 1 ENG ‫َش‬َٙ‫ظ‬ ðˤahar 1 LA01 1 ENG ‫ْش‬َٙ‫ظ‬ ðˤahər 1 LA02 1 ENG ‫ش‬ِٙ ‫م‬ َ dˤahir 1 MA01 1 ENG ‫ْش‬ٙ‫م‬ dˤhər 1 MA02 1 ENG ‫َش‬ٙ‫م‬ dˤhar 1 SA01 1 ENG ‫ش‬َٙ‫ظ‬ ðˤahr 1 ID: SWADESH_089 English word: breast Context: The mom is feeding the baby from her ___ EA01 1 ENG ‫ِعذس‬ sidr 1 EA02 1 ENG ‫ِعذس‬ sidr 1 GA01 1 ENG ‫ف ِذس‬َ sˤadir 1 GA02 1 ENG ‫ف ِذس‬ َ sˤadir 1 LA01 1 ENG ‫فْ ْذس‬ sˤədər 1 LA02 1 ENG ‫ف ِذس‬ ِ sˤidir 1 MA01 1 ENG ‫فذس‬ْ sˤdər 1 MA02 1 ENG ‫ف ْذس‬ sˤdər 1 SA01 1 ENG ‫فذس‬ َ sˤadr 1 ID: SWADESH_090 English word: heart Context: The ___ beats continuously EA01 1 ENG ‫َءٌة‬ ʔalb 1 EA02 1 ENG ‫َءٌة‬ ʔalb 1 GA01 1 ENG ‫َگٍة‬ galb 1 GA02 1 ENG ‫لٍَة‬ qalb 1 LA01 1 ENG ‫َگٍة‬ galb 1 LA02 1 ENG ‫َء ٌْة‬ ʔaləb 1 MA01 1 ENG ‫لٍَة‬ qalb 1 MA02 1 ENG ‫لٍَة‬ qalb 1 SA01 1 ENG ‫لٍَة‬ qalb 1 ID: SWADESH_091 136 English word: liver Context: The ___ filters the blood EA01 1 ENG ‫ِوثذ‬ kibd 1 EA02 1 ENG ‫ِوثذ‬ kibd 1 GA01 1 ENG ‫َوثْذ‬ kabəd 1 GA02 1 ENG ‫َچثْذ‬ ¢abəd 1 LA01 1 ENG ‫ْوثْذ‬ kəbəd 1 LA02 1 ENG ‫ْوثْذ‬ kəbəd 1 MA01 1 ENG ‫ْوثذج‬ kəbda 1 MA02 1 ENG ‫ْوثذج‬ kəbda 1 SA01 1 ENG ‫َوثِذ‬ kabid 1 ID: SWADESH_092 English word: to drink Context: Past tense form: drank. The boy ___ all the water EA01 1 ENG ‫ِؽ ِشب‬ ʃirib 1 EA02 1 ENG ‫ِؽ ِشب‬ ʃirib 1 GA01 1 ENG ‫ِؽ ِشب‬ ʃirib 1 GA02 1 ENG ‫َؽ َشب‬ ʃarab 1 LA01 1 ENG ‫ِؽشْ ب‬ ʃirəb 1 LA02 1 ENG ‫ِؽ ِشب‬ ʃirib 1 MA01 1 ENG ‫ؽشْ ب‬ ʃrəb 1 MA02 1 ENG ‫ؽشْ ب‬ ʃrəb 1 SA01 1 ENG ‫َشب‬ِ ‫ؽ‬ ʃarib 1 ID: SWADESH_093 English word: to eat Context: Past tense form: ate. The boy ___ all the food EA01 1 ENG ً‫َو‬ kal 1 EA02 1 ENG ً‫َو‬ kal 1 GA01 1 ENG ً‫َو‬ kal 1 GA01 2 ENG ً‫َء َو‬ ʔakal 1 GA02 1 ENG ً‫َء َو‬ ʔakal 1 LA01 1 ENG ً‫َء َو‬ ʔakal 1 LA02 1 ENG ً‫َء َو‬ ʔakal 1 MA01 1 ENG ‫وال‬ kla 1 MA02 1 ENG ‫وال‬ kla 1 SA01 1 ENG ً‫َء َو‬ ʔakal 1 ID: SWADESH_094 English word: to bite Context: Past tense form: bit. The boy ___ his little brother 137  EA01 1 ENG ‫ػَل‬ ʕadˤ 1 EA02 1 ENG ‫ػَل‬ ʕadˤ 1 GA01 1 ENG ‫ػَع‬ ʕaðˤ 1 GA02 1 ENG ‫ػَل‬ ʕadˤ 1 LA01 1 ENG ‫ػَع‬ ʕaðˤ 1 LA02 1 ENG ‫ػَل‬ ʕadˤ 1 MA01 1 ENG ‫ْػل‬ ʕədˤ 1 MA02 1 ENG ‫ْػل‬ ʕədˤ 1 SA01 1 ENG َّ‫ػَل‬ ʕadˤ+ 1 ID: SWADESH_095 English word: to suck Context: Past tense form: sucked. The baby ___ the bottle EA01 1 ENG ‫َِـ‬ masˤ 1 EA02 1 ENG ‫َِـ‬ masˤ 1 GA01 1 ENG ‫َِـ‬ masˤ 1 GA02 1 ENG ‫َِـ‬ masˤ 1 LA01 1 ENG ‫َِـ‬ masˤ 1 LA02 1 ENG ‫َِـ‬ masˤ 1 MA01 1 ENG ‫ِْـ‬ məsˤ 1 MA02 1 ENG ‫ِْـ‬ məsˤ 1 SA01 1 ENG َّ‫َِـ‬ masˤ+ 1 ID: SWADESH_096 English word: to spit Context: Past tense form: spat. The boy ___ on the floor EA01 1 ENG ‫ذَف‬ taf 1 EA02 1 ENG ‫ذَف‬ taf 1 GA01 1 ENG ًَ‫ذَف‬ tafal 1 GA02 1 ENG ًَ‫ذَف‬ tafal 1 LA01 1 ENG ‫ذَف‬ taf 1 LA01 2 VAR ‫تَ َضگ‬ bazag 2 LA02 1 ENG ‫َء‬،‫تَض‬ bazˤaʔ 2 MA01 1 ENG ً‫د ْف‬ dfəl 1 MA01 2 VAR ‫ت َضق‬ bzaq 2 MA02 1 ENG ‫ت َضق‬ bzaq 2 MA02 2 VAR ً‫د ْف‬ dfəl 1 SA01 1 ENG ‫قك‬ َ َ‫ت‬ basˤaq 2 ID: SWADESH_097 English word: to vomit Context: Past tense form: vomited. The boy ___ on the floor 138  EA01 1 ENG ‫َسخغ‬ rag+aʕ 1 EA01 2 VAR ‫ِءخءايح‬ ʔitʔAya 2 EA02 1 ENG ‫َسخغ‬ rag+aʕ 1 EA02 2 VAR ‫ِءخءايح‬ ʔitʔAya 2 GA01 1 ENG ‫َسخغ‬ raʤ+aʕ 1 GA01 2 VAR ‫عرَف َشؽ‬ stafraʁ 3 GA02 1 ENG ‫صَع‬ zaʕ 4 LA01 1 ENG ‫سا َخغ‬ rAʤaʕ 1 LA02 1 ENG ‫ع‬َٛ‫ذف‬ tfaw+aʕ 5 LA02 2 VAR ‫عرَف َشؽ‬ stafraʁ 3 LA02 3 VAR ‫سا َخغ‬ rAʒaʕ 1 MA01 1 ENG ‫سْ د‬ rəd 6 MA02 1 ENG ‫سْ د‬ rəd 6 MA02 2 VAR ‫َسخغ‬ raʒ+aʕ 1 SA01 1 ENG ‫ذَمَيء‬ taqay+aʔ 2 SA01 2 ENG ‫ِءعرَف َشؽ‬ ʔistafraʁ 3 ID: SWADESH_098 English word: to blow Context: Past tense form: blew. The boy ___ in the balloon EA01 1 ENG ‫َٔفَخ‬ nafax 1 EA02 1 ENG ‫َٔفَخ‬ nafax 1 GA01 1 ENG ‫ْٔفَخ‬ nəfax 1 GA02 1 ENG ‫َٔفَخ‬ nafax 1 LA01 1 ENG َ ‫َٔفخ‬ nafax 1 LA02 1 ENG ‫َٔفَخ‬ nafax 1 MA01 1 ENG ‫ٔ ْفخ‬ nfəx 1 MA02 1 ENG ‫ٔفخ‬ ْ nfəx 1 SA01 1 ENG َ ‫َٔفخ‬ nafax 1 ID: SWADESH_099 English word: to breathe Context: Past tense form: breathed. The boy ___ the air EA01 1 ENG ‫ِءذَٕفِّظ‬ ʔitnaf+is 1 EA02 1 ENG ‫ِءذَٕفِّظ‬ ʔitnaf+is 1 GA01 1 ENG ‫ْذَٕفظ‬ tənaf+as 1 GA02 1 ENG ‫ذَٕفظ‬ tnaf+as 1 LA01 1 ENG ‫ذَٕفظ‬ tnaf+as 1 LA02 1 ENG ‫ذَٕفظ‬ tnaf+as 1 MA01 1 ENG ‫ذ ْٕ ْفّظ‬ tnəf+əs 1 MA02 1 ENG ‫ذ ْٕ ْفّظ‬ tnəf+əs 1 SA01 1 ENG ‫ذََٕفظ‬ tanaf+as 1 139 ID: SWADESH_100 English word: to laugh Context: Past tense form: laughed. The boy ___ at the scene EA01 1 ENG ‫ِد ِزه‬ diħik 1 EA02 1 ENG ‫ِد ِزه‬ diħik 1 GA01 1 ENG ‫ِظ ِسه‬ ðˤiħik 1 GA02 1 ENG ‫ظَ َسه‬ ðˤaħak 1 LA01 1 ENG ‫ِظ ِسه‬ ðˤiħik 1 LA02 1 ENG ‫م ِسه‬ِ dˤiħik 1 LA02 2 ENG ‫مسه‬ dˤ+aħ+ak 1 MA01 1 ENG ‫مسْ ه‬ dˤħək 1 MA02 1 ENG ‫مسْ ه‬ dˤħək 1 SA01 1 ENG ‫م ِسه‬ َ dˤaħik 1 ID: SWADESH_101 English word: to see Context: Past tense form: saw. The boy ___ the tree EA01 1 ENG ‫ؽاف‬ ʃAf 1 EA02 1 ENG ‫ؽاف‬ ʃAf 1 GA01 1 ENG ‫ؽاف‬ ʃAf 1 GA02 1 ENG ‫چاف‬ ¢Af 1 LA01 1 ENG ‫ؽاف‬ ʃAf 1 LA02 1 ENG ‫ؽاف‬ ʃAf 1 MA01 1 ENG ‫ؽاف‬ ʃAf 1 MA02 1 ENG ‫ؽاف‬ ʃAf 1 SA01 1 ENG ‫َسءا‬ raʔA 2 ID: SWADESH_102 English word: to hear Context: Past tense form: heard. The boy ___ the music EA01 1 ENG ‫ِع ِّغ‬ simiʕ 1 EA02 1 ENG ‫ِع ِّغ‬ simiʕ 1 GA01 1 ENG ‫ِع ِّغ‬ simiʕ 1 GA02 1 ENG ‫َع َّغ‬ samaʕ 1 LA01 1 ENG ‫ِع ِّغ‬ simiʕ 1 LA02 1 ENG ‫ِع ِّغ‬ simiʕ 1 MA01 1 ENG ‫ع ّْغ‬ sməʕ 1 MA02 1 ENG ‫ع ّْغ‬ sməʕ 1 SA01 1 ENG ‫َع ِّغ‬ samiʕ 1 ID: SWADESH_103 English word: to know Context: Past tense form: knew. The boy ___ the toy 140  EA01 1 ENG ‫ِػ ِشف‬ ʕirif 1 EA02 1 ENG ‫ِػ ِشف‬ ʕirif 1 GA01 1 ENG ‫َػ َشف‬ ʕaraf 1 GA02 1 ENG ‫َػ َشف‬ ʕaraf 1 LA01 1 ENG ‫ِػ ِشف‬ ʕirif 1 LA02 1 ENG ‫ِػ ِشف‬ ʕirif 1 MA01 1 ENG ‫ػشْ ف‬ ʕrəf 1 MA02 1 ENG ‫ػشْ ف‬ ʕrəf 1 SA01 1 ENG ‫َػ َشف‬ ʕaraf 1 ID: SWADESH_104 English word: to think Context: Past tense form: thought. The boy ___ of a question EA01 1 ENG ‫فَىش‬ fak+ar 1 EA02 1 ENG ‫فَىش‬ fak+ar 1 GA01 1 ENG ‫فَىش‬ fak+ar 1 GA02 1 ENG ‫فىش‬ َ fak+ar 1 LA01 1 ENG ‫فَىش‬ fak+ar 1 LA02 1 ENG ‫فَىش‬ fak+ar 1 MA01 1 ENG ّ ‫فىش‬ ْ fk+ər 1 MA02 1 ENG ‫فىش‬ّ ْ fk+ər 1 SA01 1 ENG ‫فَىش‬ fak+ar 1 ID: SWADESH_105 English word: to smell Context: Past tense form: smelled. The boy ___ the rose EA01 1 ENG َُ‫ؽ‬ ʃam 1 EA02 1 ENG َُ‫ؽ‬ ʃam 1 GA01 1 ENG َُ‫ؽ‬ ʃam 1 GA02 1 ENG َُ‫ؽ‬ ʃam 1 LA01 1 ENG َُ‫ؽ‬ ʃam 1 LA02 1 ENG َُ‫ؽ‬ ʃam 1 MA01 1 ENG ُ‫ْؽ‬ ʃəm 1 MA02 1 ENG ُ‫ْؽ‬ ʃəm 1 SA01 1 ENG َُّ ‫َؽ‬ ʃam+ 1 ID: SWADESH_106 English word: to fear Context: Past tense form: feared. The boy ___ the dog EA01 1 ENG ‫خاف‬ xAf 1 EA02 1 ENG ‫خاف‬ xAf 1 GA01 1 ENG ‫خاف‬ xAf 1 GA02 1 ENG ‫خاف‬ xAf 1 141  LA01 1 ENG ‫خاف‬ xAf 1 LA02 1 ENG ‫خاف‬ xAf 1 MA01 1 ENG ‫خاف‬ xAf 1 MA02 1 ENG ‫خاف‬ xAf 1 SA01 1 ENG ‫خاف‬ xAf 1 ID: SWADESH_107 English word: to sleep Context: Past tense form: slept. The boy ___ EA01 1 ENG َ‫ٔا‬ nAm 1 EA02 1 ENG َ‫ٔا‬ nAm 1 GA01 1 ENG َ‫ٔا‬ nAm 1 GA01 2 VAR ‫ِس َگذ‬ rigad 2 GA02 1 ENG َ‫ٔا‬ nAm 1 LA01 1 ENG َ‫ٔا‬ nAm 1 LA02 1 ENG َ‫ٔا‬ nAm 1 MA01 1 ENG ‫ٔؼْظ‬ nʕəs 3 MA02 1 ENG ‫ٔؼْظ‬ nʕəs 3 SA01 1 ENG َ‫ٔا‬ nAm 1 SA01 2 ENG ‫َسلَذ‬ raqad 2 ID: SWADESH_108 English word: to live Context: Past tense form: lived. The hero ___ four centuries ago EA01 1 ENG ‫ػاػ‬ ʕAʃ 1 EA02 1 ENG ‫ػاػ‬ ʕAʃ 1 GA01 1 ENG ‫ػاػ‬ ʕAʃ 1 GA02 1 ENG ‫ػاػ‬ ʕAʃ 1 LA01 1 ENG ‫ػاػ‬ ʕAʃ 1 LA02 1 ENG ‫ػاػ‬ ʕAʃ 1 MA01 1 ENG ‫ػاػ‬ ʕAʃ 1 MA02 1 ENG ‫ػاػ‬ ʕAʃ 1 SA01 1 ENG ‫ػاػ‬ ʕAʃ 1 ID: SWADESH_109 English word: to die Context: Past tense form: died. The hero ___ four centuries ago EA01 1 ENG ‫ِاخ‬ mAt 1 EA01 2 VAR ‫فّا‬َٛ ‫ذ‬ twaf+a 2 EA02 1 ENG ‫ِاخ‬ mAt 1 EA02 2 VAR ّ‫فا‬َٛ ‫ذ‬ twaf+a 2 GA01 1 ENG ‫ِاخ‬ mAt 1 GA01 2 VAR ‫فّا‬َٛ ‫ذ‬ twaf+a 2 142  GA02 1 ENG ‫ِاخ‬ mAt 1 LA01 1 ENG ‫ِاخ‬ mAt 1 LA01 2 VAR ‫فّا‬َٛ ‫ذ‬ twaf+a 2 LA02 1 ENG ‫ِاخ‬ mAt 1 LA02 2 ENG ‫فّا‬َٛ ‫ذ‬ twaf+a 2 MA01 1 ENG ‫ِاخ‬ mAt 1 MA01 2 VAR ‫فّا‬ُٛ ‫ذ‬ twuf+a 2 MA02 1 ENG ‫ِاخ‬ mAt 1 MA02 2 VAR ‫فّا‬ُٛ ‫ذ‬ twuf+a 2 SA01 1 ENG ‫ِاخ‬ mAt 1 SA01 2 ENG ‫فّا‬َٛ َ‫ذ‬ tawaf+A 2 ID: SWADESH_110 English word: to kill Context: Past tense form: killed. The thief ___ the boy EA01 1 ENG ًَ‫َءذ‬ ʔatal 1 EA02 1 ENG ًَ‫َءذ‬ ʔatal 1 GA01 1 ENG ًَ‫ِگر‬ gital 1 GA01 2 ENG ‫ْرتَر‬ ðəbaħ 2 GA02 1 ENG ًَ‫لَر‬ qatal 1 LA01 1 ENG ًَ‫َگر‬ gatal 1 LA01 2 VAR ‫َرتَر‬ ðabaħ 2 LA02 1 ENG ًَ‫َءذ‬ ʔatal 1 MA01 1 ENG ً‫ل ْر‬ qtəl 1 MA02 1 ENG ً‫ل ْر‬ qtəl 1 SA01 1 ENG ًَ‫لَر‬ qatal 1 SA01 2 ENG ‫َرتَر‬ ðabaħ 2 ID: SWADESH_111 English word: to fight Context: Past tense form: fought. The boy ___ with his friend EA01 1 ENG ْ ‫ذخاْء‬ txAnəʔ 1 EA02 1 ENG ‫ذخاْء‬ْ txAnəʔ 1 GA01 1 ENG ‫ػ‬َٚ ‫ا‬ٙ‫ْذ‬ təhAwaʃ 2 GA01 2 VAR ‫ْذظا َسب‬ təðˤArab 3 GA02 1 ENG ‫ػ‬َٚ ‫ا‬ٙ‫ذ‬ thAwaʃ 2 LA01 1 ENG ‫ػ‬َٚ ‫ا‬ٙ‫ذ‬ thAwaʃ 2 LA02 1 ENG ًَ‫ءاذ‬ ʔAtal 4 LA02 2 VAR ‫ذخاَْ ء‬ txAnaʔ 1 MA01 1 ENG ‫ّداتْض‬ d+Abəz 5 MA02 1 ENG ‫داتْض‬ ّ d+Abəz 5 MA02 2 VAR ‫مّاسْ ب‬ dˤ+Arəb 3 SA01 1 ENG ًَ‫ذَماذ‬ taqAtal 4 143 ID: SWADESH_112 English word: to hunt Context: Past tense form: hunted. The man ___ in the forest EA01 1 ENG ‫فطاد‬ sˤtˤAd 1 EA02 1 ENG ‫فطاد‬ sˤtˤAd 1 GA01 1 ENG ‫فاد‬ sˤAd 1 GA02 1 ENG ‫فاد‬ sˤAd 1 LA01 1 ENG ‫قيذ‬ َ ‫ذ‬ tsˤay+ad 1 LA02 1 ENG ‫فيذ‬ َ sˤay+ad 1 LA02 2 VAR ‫فاد‬ sˤAd 1 MA01 1 ENG ْ ‫فْ يّذ‬ sˤəy+əd 1 MA02 1 ENG ْ‫فْ يّذ‬ sˤəy+əd 1 SA01 1 ENG ‫فاد‬ sˤAd 1 SA01 2 ENG ‫ِءفطاد‬ ʔisˤtˤAd 1 ID: SWADESH_113 English word: to hit Context: Past tense form: hit. The man ___ the boy EA01 1 ENG ‫م َشب‬ َ dˤarab 1 EA02 1 ENG ‫م َشب‬ َ dˤarab 1 GA01 1 ENG ‫ظَ َشب‬ ðˤarab 1 GA01 2 ENG ‫هَگ‬ tˤag 2 GA02 1 ENG ‫ظَ َشب‬ ðˤarab 1 LA01 1 ENG ‫م َشب‬ َ dˤarab 1 LA02 1 ENG ‫م َشب‬ َ dˤarab 1 MA01 1 ENG ‫مشْ ب‬ dˤrəb 1 MA02 1 ENG ‫مشْ ب‬ dˤrəb 1 SA01 1 ENG ‫م َشب‬ َ dˤarab 1 ID: SWADESH_114 English word: to cut Context: Past tense form: cut. The boy ___ the sandwitch with the knife EA01 1 ENG ‫َءهَغ‬ ʔatˤaʕ 1 EA02 1 ENG ‫َءهَغ‬ ʔatˤaʕ 1 GA01 1 ENG ‫َگـ‬ gasˤ 2 GA02 1 ENG ‫گـ‬ َ gasˤ 2 LA01 1 ENG ‫َگطَغ‬ gatˤaʕ 1 LA02 1 ENG ‫َءهَغ‬ ʔatˤaʕ 1 MA01 1 ENG ‫لطَغ‬ qtˤaʕ 1 MA02 1 ENG َ‫لطغ‬ qtˤaʕ 1 SA01 1 ENG ‫لَطَغ‬ qatˤaʕ 1 SA01 2 ENG َّ‫لَـ‬ qasˤ+ 2 144 ID: SWADESH_115 English word: to split Context: Past tense form: split. The boy ___ the cake into two equal pieces EA01 1 ENG ُ‫َء َع‬ ʔasam 1 EA02 1 ENG ُ‫َء َع‬ ʔasam 1 GA01 1 ENG ُ‫ِگ َغ‬ gisam 1 GA02 1 ENG ُ‫َگ َغ‬ gasam 1 LA01 1 ENG ُ‫گ َغ‬ َ gasam 1 LA02 1 ENG ُ‫ف‬َ ‫َء‬ ʔasˤam 1 MA01 1 ENG ‫ف َشق‬ fraq 2 MA01 2 VAR ُْ‫لغ‬ qsəm 1 MA02 1 ENG ُْ‫لغ‬ qsəm 1 SA01 1 ENG ُ‫لَ َغ‬ qasam 1 ID: SWADESH_116 English word: to stab Context: Past tense form: stabbed. He ___ the man in the heart EA01 1 ENG ‫غَض‬ ʁaz 1 EA02 1 ENG ٓ‫هَ َؼ‬ tˤaʕan 2 EA02 2 VAR ‫غَض‬ ʁaz 1 GA01 1 ENG ٓ‫هَ َؼ‬ tˤaʕan 2 GA02 1 ENG ٓ‫هَ َؼ‬ tˤaʕan 2 LA01 1 ENG ٓ‫هَ َؼ‬ tˤaʕan 2 LA02 1 ENG ٓ‫ه َؼ‬َ tˤaʕan 2 LA02 2 ENG ‫غَض‬ ʁaz 1 MA01 1 ENG ْٓ‫هؼ‬ tˤʕən 2 MA02 1 ENG ْٓ‫هؼ‬ tˤʕən 2 SA01 1 ENG ٓ‫ه َؼ‬ َ tˤaʕan 2 ID: SWADESH_117 English word: to scratch Context: Past tense form: scratched. The boy ___ his back EA01 1 ENG ‫َ٘ َشػ‬ haraʃ 1 EA02 1 ENG ‫َ٘ َشػ‬ haraʃ 1 GA01 1 ENG ‫َزه‬ ħak 2 GA02 1 ENG ‫َزه‬ ħak 2 LA01 1 ENG ‫َزه‬ ħak 2 LA02 1 ENG ‫َزه‬ ħak 2 MA01 1 ENG ‫زه‬ ħk 2 MA02 1 ENG ‫زْ ه‬ ħək 2 SA01 1 ENG َّ ‫َز‬ ‫ه‬ ħak+ 2 145 ID: SWADESH_118 English word: to dig Context: Past tense form: dug. The boy ___ a hole at the beach EA01 1 ENG ‫َزفَش‬ ħafar 1 EA02 1 ENG َ‫َزفش‬ ħafar 1 GA01 1 ENG ‫َزفَش‬ ħafar 1 GA02 1 ENG ‫َزفَش‬ ħafar 1 LA01 1 ENG ‫َزفَش‬ ħafar 1 LA02 1 ENG َ‫َزفش‬ ħafar 1 MA01 1 ENG ‫زفَش‬ ħfar 1 MA02 1 ENG ‫زفَش‬ ħfar 1 SA01 1 ENG ‫َزفَش‬ ħafar 1 ID: SWADESH_119 English word: to swim Context: Past tense form: swam. The boy ___ across the river EA01 1 ENG َ‫ػا‬ ʕAm 1 EA02 1 ENG َ‫ػا‬ ʕAm 1 GA01 1 ENG ‫ِعثَر‬ sibaħ 2 GA02 1 ENG ‫َعثَر‬ sabaħ 2 LA01 1 ENG ‫َعثَر‬ sabaħ 2 LA02 1 ENG ‫ذ َغثر‬ tsab+aħ 2 LA02 2 VAR ‫َعثَر‬ sabaħ 2 LA02 3 VAR ‫ِعثِر‬ sibiħ 2 MA01 1 ENG َ‫ػا‬ ʕAm 1 MA02 1 ENG َ‫ػا‬ ʕAm 1 SA01 1 ENG ‫َعثَر‬ sabaħ 2 SA01 2 ENG َ‫ػا‬ ʕAm 1 ID: SWADESH_120 English word: to fly Context: Past tense form: flew. The bird ___ over the building EA01 1 ENG ‫هاس‬ tˤAr 1 EA02 1 ENG ‫هاس‬ tˤAr 1 GA01 1 ENG ‫هاس‬ tˤAr 1 GA02 1 ENG ‫هاس‬ tˤAr 1 LA01 1 ENG ‫هاس‬ tˤAr 1 LA02 1 ENG ‫هاس‬ tˤAr 1 MA01 1 ENG ‫هاس‬ tˤAr 1 MA02 1 ENG ‫هاس‬ tˤAr 1 SA01 1 ENG ‫هاس‬ tˤAr 1 ID: SWADESH_121 146 English word: to walk Context: Past tense form: walked. The boy ___ in the park EA01 1 ENG ‫ِِؾي‬ miʃi 1 EA02 1 ENG ‫ِِؾي‬ miʃi 1 GA01 1 ENG ‫ِِؾا‬ miʃa 1 GA02 1 ENG ‫َِؾا‬ maʃa 1 LA01 1 ENG ‫َِؾا‬ maʃa 1 LA02 1 ENG ‫ِِؾي‬ miʃi 1 MA01 1 ENG ‫ِؾا‬ mʃa 1 MA02 1 ENG ‫ِؾا‬ mʃa 1 SA01 1 ENG ‫َِؾا‬ maʃa 1 ID: SWADESH_122 English word: to come Context: Past tense form: came. The boy ___ to the dinner EA01 1 ENG ٗ‫ِخ‬ gih 1 EA02 1 ENG ٗ‫ِخ‬ gih 1 GA01 1 ENG ‫خا‬ ʤa 1 GA02 1 ENG ‫خا‬ ʤa 1 LA01 1 ENG ‫ِءخا‬ ʔiʤa 1 LA02 1 ENG ‫ِءخا‬ ʔiʒa 1 MA01 1 ENG ‫خا‬ ʒa 1 MA02 1 ENG ‫خا‬ ʒa 1 SA01 1 ENG ‫خاء‬ ʤAʔ 1 ID: SWADESH_123 English word: to lie Context: Past tense form: lay. The boy ___ down on a bed EA01 1 ENG ‫ءع ْر َشير‬ ʔstəray+aħ 1 EA01 2 VAR ‫َِذِّد‬ mad+id 2 EA01 3 VAR ‫َسير‬ ray+aħ 1 EA02 1 ENG ‫فَ َشد‬ farad 3 EA02 2 ENG ‫َسير‬ ray+aħ 1 EA02 3 VAR ‫َِذِّد‬ mad+id 2 EA02 4 VAR ‫ءع ْر َشير‬ ʔstəray+aħ 1 GA01 1 ENG ‫ِءِ ْثطَر‬ ʔimbətˤaħ 4 GA01 2 VAR ‫ِءٔ َغذَذ‬ ʔinsadaħ 5 GA01 3 VAR ‫ْذ َّذد‬ təmad+ad 2 GA02 1 ENG ‫ٔ َغذَذ‬ nsadaħ 5 GA02 2 ENG ‫ِثَطَر‬ mbatˤaħ 4 LA01 1 ENG ‫ذ َّذد‬ tmad+ad 2 LA01 2 ENG ‫ذثَطر‬ tbatˤ+aħ 4 LA01 3 VAR ‫ذ َغطر‬ tsatˤ+aħ 6 147  LA02 1 ENG ‫ذ َغطر‬ tsatˤ+aħ 6 MA01 1 ENG ‫ذدْ ْثّذ‬ tʒəb+əd 7 MA01 2 VAR ‫ذ ّْذ‬ tməd 2 MA01 3 VAR ‫ذىا‬ tka 8 MA02 1 ENG ‫ذدْ ْثّذ‬ tʒəb+əd 7 MA02 2 VAR ‫ذىا‬ tka 8 SA01 1 ENG ‫ذَ َّذد‬ tamad+ad 2 SA01 2 ENG ‫ِءعرَشاذ‬ ʔistarAħ 1 SA01 3 ENG ‫ِءذهَء‬ ʔit+akaʔ 8 ID: SWADESH_124 English word: to sit Context: Past tense form: sat. The boy ___ on the chair EA01 1 ENG ‫َءػَذ‬ ʔaʕad 1 EA02 1 ENG ‫َءػَذ‬ ʔaʕad 1 GA01 1 ENG ‫خْ ٍَظ‬ ʤəlas 2 GA01 2 ENG ‫ْگ َؼذ‬ gəʕad 1 GA02 1 ENG ‫َگ َؼذ‬ gaʕad 1 LA01 1 ENG ‫َگ َؼذ‬ gaʕad 1 LA02 1 ENG ‫َءػَذ‬ ʔaʕad 1 MA01 1 ENG ‫گٍظ‬ ْ gləs 2 MA02 1 ENG ‫گٍظ‬ ْ gləs 2 SA01 1 ENG ‫َخٍَظ‬ ʤalas 2 SA01 2 ENG ‫لَ َؼذ‬ qaʕad 1 ID: SWADESH_125 English word: to stand Context: Past tense form: stood. The boy ___ after he was sitting EA01 1 ENG ‫ ِءف‬ِٚ wiʔif 1 EA01 2 VAR َ‫ءا‬ ʔAm 2 EA02 1 ENG ‫ ِءف‬ِٚ wiʔif 1 EA02 2 VAR َ‫ءا‬ ʔAm 2 GA01 1 ENG ‫ْ گف‬َٚ wəgaf 1 GA02 1 ENG ‫ َگف‬َٚ wagaf 1 LA01 1 ENG ‫ْ ِگف‬ٚ wəgif 1 LA01 2 VAR َ‫گا‬ gAm 2 LA02 1 ENG ‫ءْف‬ِٚ wiʔəf 1 LA02 2 VAR َ‫ءا‬ ʔAm 2 MA01 1 ENG ‫لَف‬ٚ wqaf 1 MA02 1 ENG ‫لَف‬ٚ wqaf 1 SA01 1 ENG َ ‫لف‬َٚ waqaf 1 SA01 2 ENG َ‫لا‬ qAm 2 148 ID: SWADESH_126 English word: to turn Context: Past tense form: turned. The boy ___ around the building EA01 1 ENG ‫ٌَف‬ laf 1 EA02 1 ENG ‫ٌف‬ َ laf 1 GA01 1 ENG ‫ٌَف‬ laf 1 GA01 2 VAR ‫داس‬ dAr 2 GA01 3 VAR ‫ِءفرَش‬ ʔiftar 3 GA02 1 ENG ‫فرَش‬ ftar 3 LA01 1 ENG ‫ٌَف‬ laf 1 LA01 2 VAR ‫داس‬ dAr 2 LA02 1 ENG َ‫تَ َش‬ baram 4 LA02 2 VAR ‫ٌف‬ َ laf 1 LA02 3 VAR ‫داس‬ dAr 2 MA01 1 ENG ‫داس‬ dAr 2 MA02 1 ENG ‫داس‬ dAr 2 SA01 1 ENG ‫داس‬ dAr 2 ID: SWADESH_127 English word: to fall Context: Past tense form: fell. A stone ___ from the top of a cliff EA01 1 ENG ‫ ِءع‬ِٚ wiʔiʕ 1 EA02 1 ENG ‫ ِءع‬ِٚ wiʔiʕ 1 GA01 1 ENG ‫هاذ‬ tˤAħ 2 GA02 1 ENG ‫هاذ‬ tˤAħ 2 LA01 1 ENG ‫ ْگغ‬ِٚ wigəʕ 1 LA02 1 ENG ‫ َءع‬َٚ waʔaʕ 1 LA02 2 ENG ‫ءْع‬ِٚ wiʔəʕ 1 MA01 1 ENG ‫هاذ‬ tˤAħ 2 MA02 1 ENG ‫هاذ‬ tˤAħ 2 SA01 1 ENG ‫َعمَو‬ saqatˤ 3 SA01 2 ENG ‫لَغ‬َٚ waqaʕ 1 ID: SWADESH_128 English word: to give Context: Past tense form: gave. The boy ___ the money to the man EA01 1 ENG ‫ِء ّدا‬ ʔid+a 1 EA02 1 ENG ‫ِء ّدا‬ ʔid+a 1 GA01 1 ENG ‫ْػطا‬ ʕətˤa 2 GA02 1 ENG ‫ػَطا‬ ʕatˤa 2 LA01 1 ENG ‫َءػطا‬ ʔaʕtˤa 2 LA02 1 ENG ‫ػَطا‬ ʕatˤa 2 MA01 1 ENG ‫ػطا‬ ʕtˤa 2 149  MA02 1 ENG ‫ػطا‬ ʕtˤa 2 SA01 1 ENG ‫َءػطا‬ ʔaʕtˤA 2 ID: SWADESH_129 English word: to hold Context: Past tense form: held. The boy ___ the book in his hand EA01 1 ENG ‫ِِ ِغه‬ misik 1 EA02 1 ENG ‫ِِ ِغه‬ misik 1 GA01 1 ENG ‫ِْ َغه‬ məsak 1 GA02 1 ENG ‫د‬ٛ‫َخ‬ ʤaw+ad 2 LA01 1 ENG ‫َِ َغه‬ masak 1 LA02 1 ENG ‫َ٘ ّذا‬ had+a 3 LA02 2 ENG ‫ِِ ِغه‬ misik 1 MA01 1 ENG ‫ْؽذ‬ ʃəd 4 MA02 1 ENG ‫ْؽذ‬ ʃəd 4 SA01 1 ENG ‫َِ َغه‬ masak 1 ID: SWADESH_130 English word: to squeeze Context: Past tense form: squeezed. The boy ___ the lemon EA01 1 ENG ‫قش‬ َ ‫َػ‬ ʕasˤar 1 EA02 1 ENG ‫قش‬َ ‫َػ‬ ʕasˤar 1 GA01 1 ENG ‫قش‬ َ ‫ْػ‬ ʕəsˤar 1 GA02 1 ENG ‫قش‬ َ ‫َػ‬ ʕasˤar 1 LA01 1 ENG ‫قش‬ َ ‫َػ‬ ʕasˤar 1 LA02 1 ENG ‫قش‬ َ ‫َػ‬ ʕasˤar 1 MA01 1 ENG ‫ْػقش‬ ʕəsˤ+ar 1 MA02 1 ENG ‫ْػقش‬ ʕəsˤ+ar 1 SA01 1 ENG ‫قش‬ َ ‫َػ‬ ʕasˤar 1 ID: SWADESH_131 English word: to rub Context: Past tense form: rubbed. The boy ___ the glass to clean it EA01 1 ENG ‫َدػَه‬ daʕak 1 EA02 1 ENG ‫َدػَه‬ daʕak 1 GA01 1 ENG ‫ِْ َغر‬ məsaħ 2 GA02 1 ENG ‫ف َشن‬َ farak 3 LA01 1 ENG ‫فَ َشن‬ farak 3 LA01 2 VAR ‫َدػَه‬ daʕak 1 LA02 1 ENG ‫َزف‬ ħaf 4 MA01 1 ENG ‫ِ َغر‬ msaħ 2 MA02 1 ENG ‫ِ َغر‬ msaħ 2 SA01 1 ENG ‫فَ َشن‬ farak 3 150  SA01 2 ENG ‫َدػَه‬ daʕak 1 SA01 3 ENG ‫َِ َغر‬ masaħ 2 ID: SWADESH_132 English word: to wash Context: Past tense form: washed. The boy ___ his hands EA01 1 ENG ً‫َغ َغ‬ ʁasal 1 EA02 1 ENG ً‫َغ َغ‬ ʁasal 1 GA01 1 ENG ً‫ْغغ‬ ʁəs+al 1 GA02 1 ENG ً‫َغ َغ‬ ʁasal 1 LA01 1 ENG ً‫غَغ‬ ʁas+al 1 LA01 2 ENG ً‫َغ َغ‬ ʁasal 1 LA02 1 ENG ً‫غَغ‬ ʁas+al 1 MA01 1 ENG ًْ‫غغ‬ ʁsəl 1 MA02 1 ENG ًْ‫غغ‬ ʁsəl 1 SA01 1 ENG ً‫َغ َغ‬ ʁasal 1 ID: SWADESH_133 English word: to wipe Context: Past tense form: wiped. The boy ___ the glasses with a cloth EA01 1 ENG ‫َِ َغر‬ masaħ 1 EA02 1 ENG ‫َِ َغر‬ masaħ 1 GA01 1 ENG ‫ِْ َغر‬ məsaħ 1 GA02 1 ENG ‫َِؼ‬ maʃ 2 LA01 1 ENG ‫َِ َغر‬ masaħ 1 LA02 1 ENG ‫َِ َغر‬ masaħ 1 MA01 1 ENG ‫ِ َغر‬ msaħ 1 MA02 1 ENG ‫ِ َغر‬ msaħ 1 SA01 1 ENG ‫َِ َغر‬ masaħ 1 ID: SWADESH_134 English word: to pull Context: Past tense form: pulled. The boy ___ the toy car EA01 1 ENG ‫ؽَذ‬ ʃad 1 EA02 1 ENG ‫ؽَذ‬ ʃad 1 EA02 2 VAR ‫َخش‬ gar 2 GA01 1 ENG ‫َع َسة‬ saħab 3 GA02 1 ENG ‫َع َسة‬ saħab 3 LA01 1 ENG ‫َع َسة‬ saħab 3 LA01 2 ENG ‫َخش‬ ʤar 2 LA02 1 ENG ‫ؽَذ‬ ʃad 1 LA02 2 VAR ‫َخش‬ ʒar 2 MA01 1 ENG ‫َخش‬ ʒar 2 151  MA02 1 ENG ‫َخش‬ ʒar 2 SA01 1 ENG ‫َؽ َّذ‬ ʃad+ 1 SA01 2 ENG ‫َخ َّش‬ ʤar+ 2 SA01 3 ENG ‫َع َسة‬ saħab 3 ID: SWADESH_135 English word: to push Context: Past tense form: pushed. The boy ___ the girl EA01 1 ENG ‫صَء‬ zaʔ 1 EA02 1 ENG ‫صَء‬ zaʔ 1 GA01 1 ENG ‫دَف‬ daf 2 GA02 1 ENG ‫دَص‬ daz 3 LA01 1 ENG ‫َدفَؼ‬ dafaʃ 4 LA01 2 VAR ‫دَص‬ daz 3 LA02 1 ENG ‫َدفَؼ‬ dafaʃ 4 MA01 1 ENG ‫دفَغ‬ dfaʕ 5 MA02 1 ENG ‫دفَغ‬ dfaʕ 5 SA01 1 ENG ‫َدفَغ‬ dafaʕ 5 ID: SWADESH_136 English word: to throw Context: Past tense form: threw. The boy ___ a stone EA01 1 ENG ‫َسِا‬ rama 1 EA02 1 ENG ‫َزذَف‬ ħadaf 2 EA02 2 VAR ‫َسِا‬ rama 1 GA01 1 ENG ‫َز َزف‬ ħaðaf 2 GA01 2 ENG ‫الذ‬ lAħ 3 GA01 3 ENG ًَ‫ْٔط‬ nətˤal 4 GA02 1 ENG ‫َسِا‬ rama 1 LA01 1 ENG ‫َسِا‬ rama 1 LA01 2 VAR ‫صَخ‬ zat 5 LA02 1 ENG ‫َوة‬ kab 6 LA02 2 ENG ‫صَخ‬ zat 5 LA02 3 VAR ‫َسِا‬ rama 1 MA01 1 ENG ‫سِا‬ rma 1 MA01 2 VAR ‫الذ‬ lAħ 3 MA01 3 VAR ‫ز ْذف‬ ħdəf 2 MA02 1 ENG ‫الذ‬ lAħ 3 MA02 2 VAR ‫سِا‬ rma 1 SA01 1 ENG ‫َسِا‬ ramA 1 SA01 2 ENG ‫َز َزف‬ ħaðaf 2 ID: SWADESH_137 152 English word: to tie Context: Past tense form: tied. The boy ___ the rope to the tree EA01 1 ENG ‫َستَو‬ rabatˤ 1 EA02 1 ENG ‫َستَو‬ rabatˤ 1 GA01 1 ENG ‫سْ تَو‬ rəbatˤ 1 GA02 1 ENG ‫َستَو‬ rabatˤ 1 LA01 1 ENG ‫َستَو‬ rabatˤ 1 LA01 2 VAR ‫َػ َگذ‬ ʕagəd 2 LA02 1 ENG ً‫تَى‬ bak+al 3 LA02 2 VAR ‫َستَو‬ rabatˤ 1 MA01 1 ENG ‫ستَو‬ rbatˤ 1 MA01 2 VAR ْ ‫ػمذ‬ ʕqəd 2 MA02 1 ENG ْ‫ػمذ‬ ʕqəd 2 MA02 2 VAR ‫ستَو‬ rbatˤ 1 SA01 1 ENG ‫َستَو‬ rabatˤ 1 SA01 2 ENG ‫َػمَذ‬ ʕaqad 2 ID: SWADESH_138 English word: to sew Context: Past tense form: sewed. The boy ___ the pieces of fabric EA01 1 ENG ‫خَيو‬ xay+atˤ 1 EA02 1 ENG ‫خَيو‬ xay+atˤ 1 GA01 1 ENG ‫خَيو‬ xay+atˤ 1 GA02 1 ENG ‫خَيو‬ xay+atˤ 1 LA01 1 ENG ‫خَيو‬ xay+atˤ 1 LA02 1 ENG ‫خَيو‬ xay+atˤ 1 MA01 1 ENG ‫خيِّو‬ xy+itˤ 1 MA02 1 ENG ‫خيِّو‬ xy+itˤ 1 SA01 1 ENG ‫خَيو‬ xay+atˤ 1 ID: SWADESH_139 English word: to count Context: Past tense form: counted. The boy ___ from one to ten EA01 1 ENG ‫ػَذ‬ ʕad 1 EA02 1 ENG ‫ػَذ‬ ʕad 1 GA01 1 ENG ‫َزغة‬ ħasb 2 GA02 1 ENG ‫ػَذ‬ ʕad 1 LA01 1 ENG ‫ػَذ‬ ʕad 1 LA02 1 ENG ‫ػَذ‬ ʕad 1 MA01 1 ENG ‫زغْة‬ ħsəb 2 MA02 1 ENG ‫زغْة‬ ħsəb 2 SA01 1 ENG ‫َػ َّذ‬ ʕad+ 1 153 ID: SWADESH_140 English word: to say Context: Past tense form: said. The boy ___ that he will study EA01 1 ENG ‫ءاي‬ ʔAl 1 EA02 1 ENG ‫ءاي‬ ʔAl 1 GA01 1 ENG ‫گاي‬ gAl 1 GA02 1 ENG ‫گاي‬ gAl 1 LA01 1 ENG ‫گاي‬ gAl 1 LA02 1 ENG ‫ءاي‬ ʔAl 1 MA01 1 ENG ‫گاي‬ gAl 1 MA02 1 ENG ‫گاي‬ gAl 1 MA02 2 VAR ‫لاي‬ qAl 1 SA01 1 ENG ‫لاي‬ qAl 1 ID: SWADESH_141 English word: to sing Context: Past tense form: sang. The boy ___ at the concert EA01 1 ENG ‫َغّٕا‬ ʁan+a 1 EA02 1 ENG ‫َغّٕا‬ ʁan+a 1 GA01 1 ENG ‫َغّٕا‬ ʁan+a 1 GA02 1 ENG ّ‫َغٕا‬ ʁan+a 1 LA01 1 ENG ‫َغّٕا‬ ʁan+a 1 LA02 1 ENG ‫َغّٕا‬ ʁan+a 1 MA01 1 ENG ‫َغّٕا‬ ʁan+a 1 MA02 1 ENG ّ‫َغٕا‬ ʁan+a 1 SA01 1 ENG ‫غّٕا‬ ʁn+A 1 ID: SWADESH_142 English word: to play Context: Past tense form: played. The boy ___ soccer with his friends EA01 1 ENG ‫ٌِ ِؼة‬ liʕib 1 EA02 1 ENG ‫ٌِ ِؼة‬ liʕib 1 GA01 1 ENG ‫ٌ َؼة‬ْ ləʕab 1 GA02 1 ENG ‫ٌَ َؼة‬ laʕab 1 LA01 1 ENG ‫ٌِ ِؼة‬ liʕib 1 LA02 1 ENG ‫ٌِ ِؼة‬ liʕib 1 MA01 1 ENG ‫ٌؼْة‬ lʕəb 1 MA02 1 ENG ‫ٌؼْة‬ lʕəb 1 SA01 1 ENG ‫ٌَ ِؼة‬ laʕib 1 ID: SWADESH_143 English word: to float Context: Past tense form: floated. The boat ___ on the water 154  EA01 1 ENG َ‫ػا‬ ʕAm 1 EA01 2 VAR ‫هَفا‬ tˤafa 2 EA02 1 ENG َ‫ػا‬ ʕAm 1 GA01 1 ENG َ‫ػا‬ ʕAm 1 GA02 1 ENG ‫هَفا‬ tˤafa 2 LA01 1 ENG ‫هَفا‬ tˤafa 2 LA01 2 VAR َ‫ػا‬ ʕAm 1 LA02 1 ENG ‫فاػ‬ fAʃ 3 MA01 1 ENG ‫هفا‬ tˤfa 2 MA02 1 ENG ‫هح‬ٍٛ‫ف‬ flUtˤa 4 SA01 1 ENG َ‫ػا‬ ʕAm 1 SA01 2 ENG ‫هَفا‬ tˤafA 2 ID: SWADESH_144 English word: to flow Context: Past tense form: flowed. After it rained, the water ___ on the streets EA01 1 ENG ‫ِخشي‬ giri 1 EA02 1 ENG ‫ِخشي‬ giri 1 GA01 1 ENG ‫َخشا‬ ʤara 1 GA01 2 VAR ‫ِِؾا‬ miʃa 2 GA02 1 ENG ‫غَشگ‬ ʁar+ag 3 LA01 1 ENG ‫عاي‬ sAl 4 LA02 1 ENG ‫َخشا‬ ʒara 1 MA01 1 ENG ‫خشا‬ ʒra 1 MA02 1 ENG ‫خشا‬ ʒra 1 SA01 1 ENG ‫عاي‬ sAl 4 SA01 2 ENG ‫َخشا‬ ʤarA 1 ID: SWADESH_145 English word: to freeze Context: Past tense form: froze. The cold weather ___ the water EA01 1 ENG ‫َخ ِّّذ‬ gam+id 1 EA02 1 ENG ‫ذٍح‬ ِّ َ tal+ig 2 EA02 2 VAR ‫َخ ِّّذ‬ gam+id 1 GA01 1 ENG ‫َخّذ‬ ʤam+ad 1 GA01 2 VAR ‫ثٍَح‬ θal+aʤ 2 GA02 1 ENG ‫َخّذ‬ ʤam+ad 1 GA02 2 VAR ‫ثٍَح‬ θal+aʤ 2 LA01 1 ENG ‫َخّذ‬ ʒam+ad 1 LA02 1 ENG ‫َخّذ‬ ʒam+ad 1 LA02 2 ENG ‫َخٍذ‬ ʒal+ad 1 LA02 3 VAR ‫ذٍَح‬ tal+aʒ 2 155  MA01 1 ENG ‫خّذ‬ ʒm+ad 1 MA02 1 ENG ‫خّذ‬ ʒm+ad 1 MA02 2 VAR ‫ذ ٍّْح‬ tl+əʒ 2 SA01 1 ENG ‫َخّذ‬ ʤam+ad 1 SA01 2 ENG ‫َخٍذ‬ ʤal+ad 1 ID: SWADESH_146 English word: to swell Context: Past tense form: swelled. My head ___ after I got hit EA01 1 ENG َ‫ ِس‬ِٚ wirim 1 EA02 1 ENG َ‫ ِس‬ِٚ wirim 1 GA01 1 ENG ْ ‫ِءٔرفَخ‬ ʔintəfax 2 GA02 1 ENG َ ‫ِءٔرَفخ‬ ʔintafax 2 LA01 1 ENG َ‫ ِس‬ِٚ wirim 1 LA02 1 ENG َ‫س‬َٚ war+am 1 LA02 2 VAR َ‫ ِس‬ِٚ wirim 1 MA01 1 ENG ‫ذٕفخ‬َ tnfax 2 MA02 1 ENG ‫ذٕفَخ‬ tnfax 2 SA01 1 ENG َ‫ ِس‬َٚ warim 1 SA01 2 ENG ‫ِءٔرَفَخ‬ ʔintafax 2 ID: SWADESH_147 English word: sun Context: This is the ___ EA01 1 ENG ‫ؽَّظ‬ ʃams 1 EA02 1 ENG ‫ؽَّظ‬ ʃams 1 GA01 1 ENG ‫ؽَّظ‬ ʃams 1 GA02 1 ENG ‫ؽَّظ‬ ʃams 1 LA01 1 ENG ‫ؽَّظ‬ ʃams 1 LA02 1 ENG ‫َؽ ّْظ‬ ʃaməs 1 MA01 1 ENG ‫ؽَّؼ‬ ʃamʃ 1 MA02 1 ENG ‫ؽَّؼ‬ ʃamʃ 1 SA01 1 ENG ‫ؽَّظ‬ ʃams 1 ID: SWADESH_148 English word: moon Context: This is the ___ EA01 1 ENG ‫َء َِش‬ ʔamar 1 EA02 1 ENG ‫َء َِش‬ ʔamar 1 GA01 1 ENG ‫ْگ َّش‬ gəmar 1 GA02 1 ENG ‫لَ َّش‬ qamar 1 LA01 1 ENG ‫َگ َّش‬ gamar 1 LA02 1 ENG ‫َء َِش‬ ʔamar 1 156  MA01 1 ENG ‫لَ َّش‬ qamar 1 MA02 1 ENG ‫ْگّشج‬ gəmra 1 SA01 1 ENG ‫لَ َّش‬ qamar 1 ID: SWADESH_149 English word: star Context: This is a ___ (in the sky) EA01 1 ENG ُ‫ِٔد‬ nigm 1 EA02 1 ENG ُ‫ِٔد‬ nigm 1 EA02 2 ENG ‫ْٔدّح‬ nəgma 1 GA01 1 ENG ُ‫َٔ ِد‬ naʤim 1 GA02 1 ENG ُ ْ‫َٔد‬ naʤəm 1 LA01 1 ENG ُ ْ‫ْٔد‬ nəʤəm 1 LA02 1 ENG ‫ِٔدّح‬ niʒmə 1 MA01 1 ENG ‫ْٔدّح‬ nəʒma 1 MA02 1 ENG ‫ْٔدّح‬ nəʒma 1 SA01 1 ENG ُ‫َٔد‬ naʤm 1 ID: SWADESH_150 English word: water Context: This is a cup of ___ EA01 1 ENG ‫َِيّح‬ may+a 1 EA02 1 ENG ‫َِيّح‬ may+a 1 GA01 1 ENG ‫يح‬ِٛ mUya 1 GA02 1 ENG ‫ِاي‬ mAy 1 LA01 1 ENG ‫َِي‬ may 1 LA02 1 ENG ‫َِي‬ may 1 MA01 1 ENG ‫ِا‬ ma 1 MA02 1 ENG ‫ِا‬ ma 1 SA01 1 ENG ‫ِاء‬ mAʔ 1 ID: SWADESH_151 English word: rain Context: This is ___ EA01 1 ENG ‫طش‬َ َِ matˤar 1 EA02 1 ENG ‫َِطَش‬ matˤar 1 GA01 1 ENG ‫ِْطَش‬ mətˤar 1 GA02 1 ENG ‫َِطَش‬ matˤar 1 LA01 1 ENG ‫ِؽرا‬ ʃita 2 LA02 1 ENG ‫ِؽرا‬ ʃitə 2 MA01 1 ENG ‫ؽرا‬ ʃta 2 MA02 1 ENG ‫ؽرا‬ ʃta 2 SA01 1 ENG ‫َِطَش‬ matˤar 1 157 ID: SWADESH_152 English word: river Context: This is a ___ EA01 1 ENG ‫ش‬َٙٔ nahr 1 EA02 1 ENG ‫ش‬َٙٔ nahr 1 GA01 1 ENG ‫َش‬ْٙٔ nəhar 1 GA02 1 ENG ‫َش‬َٙٔ nahar 1 LA01 1 ENG ‫ْش‬َٙٔ nahər 1 LA02 1 ENG ‫ْش‬َٙٔ nahər 1 MA01 1 ENG ‫اد‬ٚ wAd 2 MA02 1 ENG ‫اد‬ٚ wAd 2 SA01 1 ENG ‫ش‬َٙٔ nahr 1 ID: SWADESH_153 English word: lake Context: This is a ___ EA01 1 ENG ‫تُ َسيشج‬ buħayra 1 EA02 1 ENG ‫تُسيشج‬ buħIra 1 GA01 1 ENG ‫تُ َسيشج‬ buħayra 1 GA02 1 ENG ‫تُ َسيشج‬ buħayra 1 LA01 1 ENG ‫تُ َسيشج‬ buħayra 1 LA01 2 VAR ‫تْشوح‬ bərkə 2 LA02 1 ENG ‫تُ َسيشج‬ buħayra 1 MA01 1 ENG ‫تُ َسيشج‬ buħayra 1 MA02 1 ENG ‫تْشوح‬ bərka 2 MA02 2 ENG ‫تُ َسيشج‬ buħayra 1 SA01 1 ENG ‫تُ َسيشج‬ buħayra 1 SA01 2 ENG ‫تِشوح‬ birka 2 ID: SWADESH_154 English word: sea Context: This is a ___ EA01 1 ENG ‫تَسش‬ baħr 1 EA02 1 ENG ‫تَسش‬ baħr 1 GA01 1 ENG ‫تَ َسش‬ baħar 1 GA02 1 ENG ‫تَ َسش‬ baħar 1 LA01 1 ENG ‫تَ َسش‬ baħar 1 LA02 1 ENG ‫تَسْ ش‬ baħər 1 MA01 1 ENG ‫تسْ ش‬ bħər 1 MA02 1 ENG ‫تسْ ش‬ bħər 1 SA01 1 ENG ‫تَسش‬ baħr 1 158 ID: SWADESH_155 English word: salt Context: This is ___ (Mass noun) EA01 1 ENG ‫ٍَِر‬ malħ 1 EA02 1 ENG ‫ٍَِر‬ malħ 1 GA01 1 ENG ‫ٍِْر‬ məlħ 1 GA02 1 ENG ‫ٍِْر‬ məlħ 1 LA01 1 ENG ‫ِْ ٍْر‬ mələħ 1 LA02 1 ENG ‫ِْ ٍْر‬ mələħ 1 MA01 1 ENG ‫ٍِْسح‬ məlħa 1 MA02 1 ENG ‫ٍِْسح‬ məlħa 1 SA01 1 ENG ‫ٍِِر‬ milħ 1 ID: SWADESH_156 English word: stone Context: This is a ___ EA01 1 ENG ‫َز َدش‬ ħagar 1 EA02 1 ENG ‫َز َدش‬ ħagar 1 EA02 2 ENG ‫ِزداسج‬ ħigAra 1 GA01 1 ENG ‫َز َدش‬ ħaʤar 1 GA01 2 VAR ‫َزقا‬ ħasˤa 2 GA02 1 ENG ‫َز َدش‬ ħaʤar 1 LA01 1 ENG ‫َز َدش‬ ħaʤar 1 LA02 1 ENG ‫َزد ََش‬ ħaʒra 1 MA01 1 ENG ‫زْ دشج‬ ħəʒra 1 MA02 1 ENG ‫زْ دشج‬ ħəʒra 1 SA01 1 ENG ‫َز َدش‬ ħaʤar 1 SA01 2 ENG ‫َزقا‬ ħasˤA 2 ID: SWADESH_157 English word: sand Context: This is ___ (Mass noun) EA01 1 ENG ًِ‫َس‬ raml 1 EA02 1 ENG ًِ‫َس‬ raml 1 GA01 1 ENG ًِْ ‫َس‬ raməl 1 GA02 1 ENG ًِْ ‫َس‬ raməl 1 LA01 1 ENG ًِْ ‫َس‬ raməl 1 LA02 1 ENG ًِْ ‫َس‬ raməl 1 MA01 1 ENG ‫سْ ٍِح‬ rəmla 1 MA02 1 ENG ‫سْ ٍِح‬ rəmla 1 SA01 1 ENG ًِ‫َس‬ raml 1 ID: SWADESH_158 159 English word: dust Context: This is ___ (on the furniture) EA01 1 ENG ‫ذُشاب‬ turAb 1 EA02 1 ENG ‫ذُشاب‬ turAb 1 GA01 1 ENG ‫غثاس‬ ʁbAr 2 GA02 1 ENG ‫غثاس‬ ʁbAr 2 LA01 1 ENG ‫غَثشا‬ ʁabra 2 LA02 1 ENG ‫غَثشا‬ ʁabra 2 MA01 1 ENG ‫ْغثشج‬ ʁəbra 2 MA02 1 ENG ‫ْغثشج‬ ʁəbra 2 SA01 1 ENG ‫ذُشاب‬ turAb 1 SA01 2 ENG ‫ُغثاس‬ ʁubAr 2 ID: SWADESH_159 English word: earth Context: This is good ___ for growing potatoes EA01 1 ENG ‫َءسك‬ ʔardˤ 1 EA02 1 ENG ‫َءسك‬ ʔardˤ 1 GA01 1 ENG ‫َءسظ‬ ʔarðˤ 1 GA02 1 ENG ‫َءسظ‬ ʔarðˤ 1 LA01 1 ENG ‫َءسظ‬ ʔarðˤ 1 LA02 1 ENG ‫َءسْ ك‬ ʔarədˤ 1 MA01 1 ENG ‫َءسك‬ ʔardˤ 1 MA02 1 ENG ‫َءسك‬ ʔardˤ 1 SA01 1 ENG ‫َءسك‬ ʔardˤ 1 ID: SWADESH_160 English word: cloud Context: This is a ___ EA01 1 ENG ‫َعساتح‬ saħAba 1 EA02 1 ENG ‫َعساتح‬ saħAba 1 GA01 1 ENG ‫ْغيّح‬ ʁəymə 2 GA02 1 ENG ‫ْغيّح‬ ʁəymə 2 LA01 1 ENG ‫غيّح‬ ʁImə 2 LA02 1 ENG ‫غَيّح‬ ʁaymə 2 MA01 1 ENG ‫عساتح‬ sħAba 1 MA01 2 VAR ‫غيّح‬ ʁIma 2 MA02 1 ENG ‫عساتح‬ sħAba 1 MA02 2 VAR ‫غيّح‬ ʁIma 2 SA01 1 ENG ‫َعساتح‬ saħAba 1 SA01 2 ENG ‫غَيّح‬ ʁayma 2 ID: SWADESH_161 160 English word: fog Context: This is a ___ EA01 1 ENG َ‫غَيا‬ ʁayAm 1 EA02 1 ENG ‫مثاب‬َ dˤabAb 2 GA01 1 ENG ‫ظَثاب‬ ðˤabAb 2 GA02 1 ENG ‫ظَثاب‬ ðˤabAb 2 LA01 1 ENG ‫ظَثاب‬ ðˤabAb 2 LA02 1 ENG ‫مثاب‬ َ dˤabAb 2 MA01 1 ENG ‫مثاب‬ dˤbAb 2 MA02 1 ENG ‫مثاب‬ dˤbAb 2 SA01 1 ENG ‫مثاب‬ َ dˤabAb 2 ID: SWADESH_162 English word: sky Context: The __ is blue EA01 1 ENG ‫َعّا‬ sama 1 EA02 1 ENG ‫َعّا‬ sama 1 GA01 1 ENG ‫عّْا‬ səma 1 GA02 1 ENG ‫َعّا‬ sama 1 LA01 1 ENG ‫َعّا‬ sama 1 LA02 1 ENG ‫َعّا‬ sama 1 MA01 1 ENG ‫عّا‬ sma 1 MA02 1 ENG ‫عّا‬ sma 1 SA01 1 ENG ‫َعّاء‬ samAʔ 1 ID: SWADESH_163 English word: wind Context: The ___ is blowing EA01 1 ENG ‫ا‬َٛ٘ hawa 1 EA02 1 ENG ‫ا‬َٛ٘ hawa 1 GA01 1 ENG ‫سير‬ rIħ 2 GA01 2 VAR ‫ا‬َٛ٘ hawa 1 GA02 1 ENG ‫ا‬َٛ٘ hawa 1 LA01 1 ENG ‫سير‬ rIħ 2 LA02 1 ENG ‫ا‬َٛ٘ hawa 1 MA01 1 ENG ‫سير‬ rIħ 2 MA02 1 ENG ‫سير‬ rIħ 2 MA02 2 VAR ‫ا‬ٛ٘ hwa 1 SA01 1 ENG ‫سير‬ rIħ 2 SA01 2 ENG ‫اء‬َٛ٘ hawAʔ 1 ID: SWADESH_164 English word: snow 161 Context: The ___ falling EA01 1 ENG ‫ذٍَح‬ talg 1 EA02 1 ENG ‫ذٍَح‬ talg 1 GA01 1 ENG ‫ثٍَح‬ θalʤ 1 GA02 1 ENG ‫ثٍَح‬ θalʤ 1 LA01 1 ENG ‫ذٍَِح‬ taliʒ 1 LA02 1 ENG ‫ذٍَِح‬ taliʒ 1 MA01 1 ENG ‫ْذٍح‬ təlʒ 1 MA02 1 ENG ‫ْذٍح‬ təlʒ 1 SA01 1 ENG ‫ثٍَح‬ θalʤ 1 ID: SWADESH_165 English word: ice Context: This is a cube of ___ EA01 1 ENG ‫ذٍَح‬ talg 1 EA02 1 ENG ‫ذٍَح‬ talg 1 GA01 1 ENG ‫ثٍَح‬ θalʤ 1 GA02 1 ENG ‫ثٍَح‬ θalʤ 1 LA01 1 ENG ‫ثٍَح‬ θalʤ 1 LA02 1 ENG ‫ذٍَِح‬ taliʒ 1 MA01 1 ENG ‫ذٍح‬ tlʒ 1 MA02 1 ENG ٛ‫گالف‬ glAsˤu 2 SA01 1 ENG ‫ثٍَح‬ θalʤ 1 ID: SWADESH_166 English word: smoke Context: There may be a fire in that house, ___ is coming out of the windows EA01 1 ENG ْ‫ُد ّخا‬ dux+An 1 EA02 1 ENG ْ‫دخا‬ّ ُ dux+An 1 GA01 1 ENG ْ‫ْد ّخا‬ dəx+An 1 GA02 1 ENG ْ‫ْد ّخا‬ dəx+An 1 LA01 1 ENG ْ‫ُد ّخا‬ dux+An 1 LA02 1 ENG ْ‫دخا‬ ّ ْ dəx+An 1 MA01 1 ENG ْ‫ْد ّخا‬ dəx+An 1 MA02 1 ENG ْ‫ْد ّخا‬ dəx+An 1 SA01 1 ENG ْ‫دُخا‬ duxAn 1 ID: SWADESH_167 English word: fire Context: This is a ___ (in the grill) EA01 1 ENG ‫ٔاس‬ nAr 1 EA02 1 ENG ‫ٔاس‬ nAr 1 162  GA01 1 ENG ‫ٔاس‬ nAr 1 GA02 1 ENG ‫ٔاس‬ nAr 1 LA01 1 ENG ‫ٔاس‬ nAr 1 LA02 1 ENG ‫ٔاس‬ nAr 1 MA01 1 ENG ‫ػافيح‬ ʕAfya 2 MA02 1 ENG ‫ػافيح‬ ʕAfya 2 SA01 1 ENG ‫ٔاس‬ nAr 1 ID: SWADESH_168 English word: ash Context: This is ___ (in the grill) EA01 1 ENG ‫َسِاد‬ ramAd 1 EA02 1 ENG ‫َسِاد‬ ramAd 1 GA01 1 ENG ‫سْ ِاد‬ rəmAd 1 GA02 1 ENG ‫سِاد‬ rmAd 1 LA01 1 ENG ٓ‫َع َچ‬ sa¢an 2 LA02 1 ENG ‫ج‬ٛ‫فف‬َ sˤafwə 3 MA01 1 ENG ‫سِاد‬ rmAd 1 MA02 1 ENG ‫سِاد‬ rmAd 1 SA01 1 ENG ‫َسِاد‬ ramAd 1 ID: SWADESH_169 English word: to burn Context: Past tense form: burned. The grill ___ the boy's hand EA01 1 ENG ‫َز َشء‬ ħaraʔ 1 EA02 1 ENG ‫َز َشء‬ ħaraʔ 1 GA01 1 ENG ‫َز َشگ‬ ħarag 1 GA02 1 ENG ‫َز َشگ‬ ħarag 1 LA01 1 ENG ‫َز َشگ‬ ħarag 1 LA02 1 ENG ‫َز َشء‬ ħaraʔ 1 MA01 1 ENG ‫زشْ ق‬ ħrəq 1 MA02 1 ENG ‫زشْ گ‬ ħrəg 1 SA01 1 ENG ‫َز َشق‬ ħaraq 1 ID: SWADESH_170 English word: road Context: This is a nice ___ between the two cities EA01 1 ENG ‫هَشيء‬ tˤarIʔ 1 EA02 1 ENG ‫هَشيء‬ tˤarIʔ 1 GA01 1 ENG ‫هشيگ‬ ْ tˤərIg 1 GA02 1 ENG ‫هَشيگ‬ tˤarIg 1 LA01 1 ENG ‫هشيگ‬ ْ tˤərIg 1 LA02 1 ENG ‫هشيء‬َ tˤarIʔ 1 163  MA01 1 ENG ‫هشيك‬ tˤrIq 1 MA02 1 ENG ‫هشيك‬ tˤrIq 1 SA01 1 ENG ‫هَشيك‬ tˤarIq 1 ID: SWADESH_171 English word: mountain Context: This is a nice ___ it is very big and covered with snow EA01 1 ENG ًَ‫َخث‬ gabal 1 EA02 1 ENG ًَ‫َخث‬ gabal 1 GA01 1 ENG ًَ‫خْ ث‬ ʤəbal 1 GA02 1 ENG ًَ‫َخث‬ ʤabal 1 LA01 1 ENG ًَ‫َخث‬ ʤabal 1 LA02 1 ENG ًَ‫َخث‬ ʒabal 1 MA01 1 ENG ًْ‫خث‬ ʒbəl 1 MA02 1 ENG ًْ‫خث‬ ʒbəl 1 SA01 1 ENG ًَ‫َخث‬ ʤabal 1 ID: SWADESH_172 English word: red Context: The color is ___ EA01 1 ENG ‫َءز َّش‬ ʔaħmar 1 EA02 1 ENG ‫َءز َّش‬ ʔaħmar 1 GA01 1 ENG ‫َءز َّش‬ ʔaħmar 1 GA02 1 ENG ‫َءز َّش‬ ʔaħmar 1 LA01 1 ENG ‫َءز َّش‬ ʔaħmar 1 LA02 1 ENG ‫َءز َّش‬ ʔaħmar 1 MA01 1 ENG ‫ز َّش‬ ħmar 1 MA02 1 ENG ‫ز َّش‬ ħmar 1 SA01 1 ENG ‫َءز َّش‬ ʔaħmar 1 ID: SWADESH_173 English word: green Context: The color is ___ EA01 1 ENG ‫نش‬ َ ‫َءخ‬ ʔaxdˤar 1 EA02 1 ENG ‫نش‬َ ‫َءخ‬ ʔaxdˤar 1 GA01 1 ENG ‫َءخظَش‬ ʔaxðˤar 1 GA02 1 ENG ‫َءخظَش‬ ʔaxðˤar 1 LA01 1 ENG ‫َءخظَش‬ ʔaxðˤar 1 LA02 1 ENG ‫نش‬ َ ‫َءخ‬ ʔaxdˤar 1 MA01 1 ENG ‫نش‬ َ ‫خ‬ xdˤar 1 MA02 1 ENG ‫نش‬ َ ‫خ‬ xdˤar 1 SA01 1 ENG ‫نش‬ َ ‫َءخ‬ ʔaxdˤar 1 164 ID: SWADESH_174 English word: yellow Context: The color is ___ EA01 1 ENG ‫َءففَش‬ ʔasˤfar 1 EA02 1 ENG ‫َءففَش‬ ʔasˤfar 1 GA01 1 ENG ‫َءففَش‬ ʔasˤfar 1 GA02 1 ENG ‫َءففَش‬ ʔasˤfar 1 LA01 1 ENG ‫َءففَش‬ ʔasˤfar 1 LA02 1 ENG ‫َءففَش‬ ʔasˤfar 1 MA01 1 ENG ‫ففَش‬ sˤfar 1 MA02 1 ENG ‫ففَش‬ sˤfar 1 SA01 1 ENG ‫َءففَش‬ ʔasˤfar 1 ID: SWADESH_175 English word: white Context: The color is ___ EA01 1 ENG ‫َءتيَل‬ ʔabyadˤ 1 EA02 1 ENG ‫َءتيَل‬ ʔabyadˤ 1 GA01 1 ENG ‫َءتيَع‬ ʔabyaðˤ 1 GA02 1 ENG ‫َءتيَع‬ ʔabyaðˤ 1 LA01 1 ENG ‫َءتيَل‬ ʔabyadˤ 1 LA02 1 ENG ‫َءتيَل‬ ʔabyadˤ 1 MA01 1 ENG ‫تيْل‬ byədˤ 1 MA02 1 ENG ‫تيْل‬ byədˤ 1 SA01 1 ENG ‫َءتيَل‬ ʔabyadˤ 1 ID: SWADESH_176 English word: black Context: The color is ___ EA01 1 ENG ‫د‬ٛ‫ع‬ ِ ‫ِء‬ ʔiswid 1 EA02 1 ENG ‫د‬ٛ‫ع‬ ِ ‫ِء‬ ʔiswid 1 GA01 1 ENG ‫د‬َٛ ‫َءع‬ ʔaswad 1 GA02 1 ENG ‫د‬َٛ ‫َءع‬ ʔaswad 1 LA01 1 ENG ‫د‬َٛ ‫َءع‬ ʔaswad 1 LA02 1 ENG ‫د‬َٛ ‫َءع‬ ʔaswad 1 MA01 1 ENG ً ْ‫وس‬ kħəl 2 MA02 1 ENG ً ْ‫وس‬ kħəl 2 SA01 1 ENG ‫د‬َٛ ‫َءع‬ ʔaswad 1 ID: SWADESH_177 English word: night Context: Indefinit singular form, During the Summer, the ___ is short EA01 1 ENG ً‫ٌي‬ lIl 1 165  EA02 1 ENG ً‫ٌي‬ lIl 1 GA01 1 ENG ً‫ٌي‬ lIl 1 GA02 1 ENG ً‫ٌي‬ lIl 1 LA01 1 ENG ً‫ٌي‬ lIl 1 LA02 1 ENG ً‫ٌي‬ lIl 1 MA01 1 ENG ً‫ٌي‬ lIl 1 MA02 1 ENG ً‫ٌي‬ lIl 1 SA01 1 ENG ً‫ٌَي‬ layl 1 ID: SWADESH_178 English word: day Context: Indefinit singular form, During the Winter, the ___ is short EA01 1 ENG َٛ‫ي‬ yUm 1 EA01 2 VAR ‫اس‬َٙٔ nahAr 2 EA02 1 ENG ‫اس‬َٙٔ nahAr 2 EA02 2 VAR َٛ‫ي‬ yUm 1 GA01 1 ENG ‫اس‬َٙٔ nahAr 2 GA02 1 ENG َٛ‫ي‬ yUm 1 LA01 1 ENG ‫اس‬ٙٔ nhAr 2 LA02 1 ENG ‫اس‬ٙٔ nhAr 2 MA01 1 ENG ‫اس‬ٙٔ nhAr 2 MA02 1 ENG ‫اس‬ٙٔ nhAr 2 SA01 1 ENG ‫اس‬َٙٔ nahAr 2 ID: SWADESH_179 English word: year Context: Indefinit singular form, The ___ 2012 was happy EA01 1 ENG ‫َعٕح‬ sana 1 EA02 1 ENG ‫َعٕح‬ sana 1 GA01 1 ENG ‫َعٕح‬ sana 1 GA02 1 ENG ‫َعٕح‬ sana 1 LA01 1 ENG ‫َعٕح‬ sanə 1 LA02 1 ENG ‫ِعٕح‬ sinə 1 MA01 1 ENG َ‫ػا‬ ʕAm 2 MA02 1 ENG َ‫ػا‬ ʕAm 2 SA01 1 ENG ‫َعٕح‬ sana 1 SA01 2 ENG َ‫ػا‬ ʕAm 2 ID: SWADESH_180 English word: warm Context: The food is not cold and it is not hot, it is ___ EA01 1 ENG ‫دافي‬ dAfi 1 EA02 1 ENG ‫دافي‬ dAfi 1 166  GA01 1 ENG ‫دافي‬ dAfi 1 GA02 1 ENG ‫دافي‬ dAfi 1 LA01 1 ENG ‫دافي‬ dAfi 1 LA02 1 ENG ‫دافي‬ dAfi 1 MA01 1 ENG ‫دافي‬ dAfi 1 MA02 1 ENG ‫دافي‬ dAfi 1 SA01 1 ENG ‫دافء‬ ِ dAfiʔ 1 ID: SWADESH_181 English word: cold Context: The weather is ___ outside, wear a coat before going out EA01 1 ENG ‫عا ِءع‬ sAʔiʕ 1 EA01 2 VAR ‫تَشد‬ bard 2 EA02 1 ENG ‫تَشد‬ bard 2 EA02 2 VAR ‫عا ِءع‬ sAʔiʕ 1 EA02 3 VAR ‫طءػح‬ َ saʔʕa 1 GA01 1 ENG ‫تاسد‬ ِ bArid 2 GA02 1 ENG ‫تاسد‬ ِ bArid 2 LA01 1 ENG ‫تاسد‬ ِ bArid 2 LA01 2 VAR ‫َعگؼح‬ sagʕa 1 LA02 1 ENG ‫تاسد‬ ِ bArid 2 LA02 2 VAR ‫ؿءػح‬ َ sˤaʔʕə 1 MA01 1 ENG ‫تاسْ د‬ bArəd 2 MA02 1 ENG ‫تاسْ د‬ bArəd 2 SA01 1 ENG ‫تاسد‬ ِ bArid 2 ID: SWADESH_182 English word: full Context: This cup is ___ EA01 1 ENG ْ‫ٍَِيا‬ malyAn 1 EA02 1 ENG ْ‫ٍَِيا‬ malyAn 1 GA01 1 ENG ْ‫ٍَِيا‬ malyAn 1 GA02 1 ENG ْ‫ٍَِيا‬ malyAn 1 GA02 2 VAR ‫ط‬ٚ‫َِرش‬ matrUs 2 LA01 1 ENG ْ‫ٍَِيا‬ malyAn 1 LA02 1 ENG ْ‫ٍَِيا‬ malyAn 1 MA01 1 ENG ‫ػا ِْش‬ ʕAmər 3 MA02 1 ENG ‫ػا ِْش‬ ʕAmər 3 SA01 1 ENG ْ‫ًَِءا‬ malʔAn 1 ID: SWADESH_183 English word: new Context: This is a ___ 167 shoe EA01 1 ENG ‫ِخذيذ‬ gidId 1 EA02 1 ENG ‫ِخذيذ‬ gidId 1 GA01 1 ENG ‫خْ ذيذ‬ ʤədId 1 GA02 1 ENG ‫َخذيذ‬ ʤadId 1 LA01 1 ENG ‫خذيذ‬ ʒdId 1 LA02 1 ENG ‫خذيذ‬ ʒdId 1 MA01 1 ENG ‫خذيذ‬ ʒdId 1 MA02 1 ENG ‫خذيذ‬ ʒdId 1 SA01 1 ENG ‫َخذيذ‬ ʤadId 1 ID: SWADESH_184 English word: old Context: This is an ___ shoe EA01 1 ENG ُ‫َءدي‬ ʔadIm 1 EA02 1 ENG ُ‫َءدي‬ ʔadIm 1 GA01 1 ENG ُ‫ْگذي‬ gədIm 1 GA02 1 ENG ُ‫لَذي‬ qadIm 1 LA01 1 ENG ُ‫َگذي‬ gadIm 1 LA02 1 ENG ُ‫َءدي‬ ʔadIm 1 MA01 1 ENG ُ‫لذي‬ qdIm 1 MA02 1 ENG ُ‫لذي‬ qdIm 1 SA01 1 ENG ُ‫لَذي‬ qadIm 1 ID: SWADESH_185 English word: good Context: This is a ___ shoe, its comfortable EA01 1 ENG ‫يِّظ‬َٛ ‫و‬ kway+is 1 EA02 1 ENG ٍٛ‫ِز‬ ħilu 2 EA02 2 VAR ‫يِّظ‬َٛ ‫و‬ kway+is 1 GA01 1 ENG ٓ‫صي‬ zIn 3 GA02 1 ENG ٓ‫صي‬ zIn 3 LA01 1 ENG ‫ٍِير‬ mlIħ 4 LA01 2 ENG ‫يِّظ‬َٛ ‫و‬ kway+is 1 LA02 1 ENG ‫ِٕير‬ mnIħ 4 MA01 1 ENG ْ‫ِضيا‬ mzyAn 3 MA02 1 ENG ْ‫ِضيا‬ mzyAn 3 SA01 1 ENG ‫َخيِّذ‬ ʤay+id 5 ID: SWADESH_186 English word: bad Context: This is a ___ shoe, its very uncomfortable EA01 1 ENG ‫ ِزؼ‬ِٚ wiħiʃ 1 168  EA02 1 ENG ‫ ِزؼ‬ِٚ wiħiʃ 1 GA01 1 ENG ‫ْع ِّيء‬ səy+iʔ 2 GA02 1 ENG ٓ‫صي‬ِٛ mUzIn 3 LA01 1 ENG ً‫ػا ِه‬ ʕatˤil 4 LA02 1 ENG ‫َع ِّيء‬ say+iʔ 2 MA01 1 ENG ‫خاية‬ xAyb 5 MA02 1 ENG ‫خاية‬ xAyb 5 SA01 1 ENG ‫َع ِّيء‬ say+iʔ 2 ID: SWADESH_187 English word: rotten Context: Don’t eat the bread or apple, Its ___ (its color has changed) EA01 1 ENG ِّٓ‫ِِ َؼف‬ miʕaf+in 1 EA01 2 VAR ،‫تايِض‬ bAyizˤ 2 EA02 1 ENG ،‫تايِض‬ bAyizˤ 2 EA02 2 VAR ِّ ٓ‫ِِ َؼف‬ miʕaf+in 1 GA01 1 ENG ٓ‫ِ َؼف‬ِّ mʕaf+in 1 GA02 1 ENG َٓ‫َػف‬ ʕafan 1 LA01 1 ENG ِّٓ‫ِ َؼف‬ mʕaf+in 1 LA01 2 VAR ‫ِ َخ ِّّح‬ mxam+iʤ 3 LA02 1 ENG ٓ‫ِ َؼف‬ mʕaf+an 1 MA01 1 ENG ‫خغْش‬ xsər 4 MA01 2 VAR ‫خا ِِح‬ xAmiʒ 3 MA02 1 ENG ‫خاية‬ xAyb 5 MA02 2 ENG ‫خا ِِح‬ xAmiʒ 3 SA01 1 ENG ِّٓ‫ُِرَ َؼف‬ mutaʕaf+in 1 ID: SWADESH_188 English word: dirty Context: This chair is ___ EA01 1 ENG ‫ ِعخ‬ِٚ wisix 1 EA02 1 ENG ‫ ِعخ‬ِٚ wisix 1 GA01 1 ENG ‫فْ خ‬َٚ wasˤəx 1 GA02 1 ENG ‫ْ فْ خ‬ٚ wəsˤəx 1 LA01 1 ENG ‫ْ فْ خ‬ٚ wəsˤəx 1 LA02 1 ENG ‫عخ‬َٛ ِ mwas+ax 1 LA02 2 VAR ‫ ِعخ‬ِٚ wisix 1 MA01 1 ENG ‫عخ‬َٛ ِ mwas+ax 1 MA02 1 ENG ‫عّخ‬َٛ ِ mwas+ax 1 SA01 1 ENG ‫ ِعخ‬َٚ wasix 1 ID: SWADESH_189 English word: straight 169 Context: draw a ___ line between the points EA01 1 ENG ُ‫ِْغرَمي‬ məstaqIm 1 EA02 1 ENG ُ‫ِْغرَمي‬ məstaqIm 1 GA01 1 ENG ُ‫ِْغرَمي‬ məstaqIm 1 GA02 1 ENG ‫عيذا‬ sIda 2 LA01 1 ENG ُ‫ُِغرَمي‬ mustaqIm 1 LA02 1 ENG ‫خاٌِظ‬ ʒAlis 3 MA01 1 ENG ُ‫ُِغرَمي‬ mustaqIm 1 MA02 1 ENG ً‫ي‬ٛ‫ه‬ tˤwIl 4 SA01 1 ENG ُ‫ُِغرَمي‬ mustaqIm 1 ID: SWADESH_190 English word: round Context: Masculine form, This is a ___ dish EA01 1 ENG ‫س‬َٚ‫ِْذ‬ mədaw+ar 1 EA02 1 ENG ‫س‬َٚ‫ِْذ‬ mədaw+ar 1 GA01 1 ENG ‫دا ِءسي‬ dAʔiri 1 GA02 1 ENG ‫دا ِءسي‬ dAʔiri 1 LA01 1 ENG ‫س‬َٚ‫ِذ‬ mdaw+ar 1 LA02 1 ENG ‫س‬َٚ‫ِذ‬ mdaw+ar 1 MA01 1 ENG ‫س‬ٚ‫ِ ْذ‬ mdəw+ar 1 MA01 2 VAR ‫دا ِءسي‬ dAʔiri 1 MA02 1 ENG ‫س‬ٛ ْ‫ِن‬ mdˤəw+ar 1 SA01 1 ENG ‫دا ِءسي‬ dAʔiri 1 ID: SWADESH_191 English word: sharp Context: This is a ___ axe EA01 1 ENG ‫زاد‬ ħAd 1 EA01 2 VAR ‫زاِي‬ ħAmi 2 EA02 1 ENG ‫زاِي‬ ħAmi 2 GA01 1 ENG ‫زاد‬ ħAd 1 GA02 1 ENG ‫زاد‬ ħAd 1 LA01 1 ENG ‫ِاظي‬ mAðˤi 3 LA02 1 ENG ‫ط‬ٚ‫ِ َش‬ mraw+as 4 LA02 2 ENG ‫َزذ‬ ħad 1 MA01 1 ENG ‫ِامي‬ mAdˤi 3 MA02 1 ENG ‫ِامي‬ mAdˤi 3 SA01 1 ENG ‫ِاك‬ mAdˤ 3 SA01 2 ENG ‫زاد‬ ħAd 1 ID: SWADESH_192 English word: dull 170 Context: This is a ___ axe EA01 1 ENG ٍُِِ‫ذ‬ tilim 1 EA02 1 ENG ‫تاسد‬ ِ bArid 2 EA02 2 VAR ٍُِِ‫ذ‬ tilim 1 GA01 1 ENG ‫تساد‬ِٛٙ mhUbħAd 3 GA01 2 VAR ‫ُِؾساد‬ muʃħAd 3 GA02 1 ENG ‫زاد‬ِٛ mUħAd 3 LA01 1 ENG ‫ِْؾّاظي‬ məʃmAðˤi 4 LA02 1 ENG ‫ َزذ‬َِّٕٛ man+Uħad 3 MA01 1 ENG ‫زافي‬ ħAfi 5 MA02 1 ENG ‫زافي‬ ħAfi 5 SA01 1 ENG ‫تاسد‬ ِ bArid 2 SA01 2 ENG ٌُِ‫ثا‬ θAlim 1 ID: SWADESH_193 English word: smooth Context: This is a ___ surface EA01 1 ENG ُ‫ٔا ِػ‬ nAʕim 1 EA02 1 ENG ُ‫ٔا ِػ‬ nAʕim 1 GA01 1 ENG ‫َءٍَِظ‬ ʔamlas 2 GA01 2 VAR ُ‫ٔا ِػ‬ nAʕim 1 GA02 1 ENG ُ‫ٔا ِػ‬ nAʕim 1 LA01 1 ENG ُ‫ٔا ِػ‬ nAʕim 1 LA01 2 ENG ‫ٍِِْظ‬ məlis 2 LA02 1 ENG ‫ِاٌِظ‬ mAlis 2 LA02 2 VAR ُ‫ٔا ِػ‬ nAʕim 1 MA01 1 ENG ‫سهة‬ْ rtˤəb 3 MA01 2 VAR ‫ِْ ٍْظ‬ mələs 2 MA02 1 ENG ‫سهة‬ ْ rtˤəb 3 SA01 1 ENG ‫َءٍَِظ‬ ʔamlas 2 SA01 2 ENG ‫ٍَِِظ‬ malis 2 SA01 3 ENG ُ‫ٔا ِػ‬ nAʕim 1 ID: SWADESH_194 English word: wet Context: This is a ___ cloth, it is dripping water EA01 1 ENG ‫ي‬ٍٛ‫َِث‬ mablUl 1 EA02 1 ENG ‫ي‬ٍٛ‫َِث‬ mablUl 1 GA01 1 ENG ‫ي‬ٍٛ‫ِْث‬ məblUl 1 GA02 1 ENG ‫سْ ِهة‬ rətˤib 2 LA01 1 ENG ‫ي‬ٍٛ‫َِث‬ mablUl 1 LA02 1 ENG ًٍَ‫ِث‬ mbal+al 1 MA01 1 ENG ‫فاصگ‬ fAzg 3 171  MA02 1 ENG ‫فاصگ‬ fAzg 3 SA01 1 ENG ً‫تٍَي‬ balIl 1 SA01 2 ENG ًَ‫ُِثر‬ mubtal 1 ID: SWADESH_195 English word: dry Context: This is a ___ cloth EA01 1 ENG ‫ٔا ِؽف‬ nAʃif 1 EA02 1 ENG ‫ٔا ِؽف‬ nAʃif 1 GA01 1 ENG ‫ٔا ِؽف‬ nAʃif 1 GA02 1 ENG ‫ٔا ِؽف‬ nAʃif 1 GA02 2 ENG ‫خاف‬ ʤAf 2 LA01 1 ENG ‫ٔا ِؽف‬ nAʃif 1 LA02 1 ENG ‫ٔا ِؽف‬ nAʃif 1 MA01 1 ENG ‫ٔاؽف‬ nAʃf 1 MA02 1 ENG ‫ٔاؽف‬ nAʃf 1 SA01 1 ENG ‫خاف‬ ʤAf 2 SA01 2 ENG ‫ٔا ِؽف‬ nAʃif 1 ID: SWADESH_196 English word: correct Context: Masculine form, This is a ___ answer EA01 1 ENG ‫فسير‬ َ sˤaħIħ 1 EA01 2 VAR ‫ه‬ٛ‫َِضت‬ mazbUtˤ 2 EA01 3 VAR ‫فر‬ َ sˤaħ 1 EA02 1 ENG ‫فر‬ َ sˤaħ 1 EA02 2 VAR ‫فسير‬ َ sˤaħIħ 1 EA02 3 VAR ‫ه‬ٛ‫َِضت‬ mazbUtˤ 2 GA01 1 ENG ‫ْػ ِذي‬ ʕədil 3 GA01 2 ENG ‫فسير‬ َ sˤaħIħ 1 GA02 1 ENG ‫فسير‬ َ sˤaħIħ 1 LA01 1 ENG ‫فسير‬ َ sˤaħIħ 1 LA01 2 VAR ‫ه‬ٛ‫َِضت‬ mazbUtˤ 2 LA02 1 ENG ‫فر‬ َ sˤaħ 1 LA02 2 VAR ‫فسير‬ َ sˤaħIħ 1 LA02 3 VAR ‫ه‬ٛ‫َِضت‬ mazbUtˤ 2 MA01 1 ENG ‫فسير‬ sˤħIħ 1 MA02 1 ENG ‫ف ِسر‬ sˤħiħ 1 SA01 1 ENG ‫فسير‬ َ sˤaħIħ 1 ID: SWADESH_197 English word: near 172 Context: The river is ___ my house EA01 1 ENG ‫ُء َسيِّة‬ ʔuray+ib 1 EA02 1 ENG ‫ُء َسيِّة‬ ʔuray+ib 1 GA01 1 ENG ‫گشية‬ grIb 1 GA02 1 ENG ‫َگشية‬ garIb 1 LA01 1 ENG ‫َگشية‬ garIb 1 LA02 1 ENG ‫َءسية‬ ʔarIb 1 MA01 1 ENG ‫لشية‬ qrIb 1 MA02 1 ENG ‫لشية‬ qrIb 1 SA01 1 ENG ‫لَشية‬ qarIb 1 ID: SWADESH_198 English word: far Context: The river is ___ from my house EA01 1 ENG ‫تِؼيذ‬ biʕId 1 EA02 1 ENG ‫تِؼيذ‬ biʕId 1 GA01 1 ENG ‫تؼيذ‬ bʕId 1 GA02 1 ENG ‫تَؼيذ‬ baʕId 1 LA01 1 ENG ‫تؼيذ‬ bʕId 1 LA02 1 ENG ‫تؼيذ‬ bʕId 1 MA01 1 ENG ‫تؼيذ‬ bʕId 1 MA02 1 ENG ‫تؼيذ‬ bʕId 1 SA01 1 ENG ‫تَؼيذ‬ baʕId 1 ID: SWADESH_199 English word: right Context: I am to the ___ of the boy (Looking at a picture of boys) EA01 1 ENG ٓ‫يّْي‬ yəmIn 1 EA02 1 ENG ٓ‫يِّي‬ yimIn 1 GA01 1 ENG ٓ‫يّْي‬ yəmIn 1 GA02 1 ENG ٓ‫يَّي‬ yamIn 1 LA01 1 ENG ٓ‫يَّي‬ yamIn 1 LA02 1 ENG ٓ‫يَّي‬ yamIn 1 MA01 1 ENG ّْٓ ‫ٌي‬ lImən 1 MA02 1 ENG ّْٓ ‫ٌي‬ lImən 1 SA01 1 ENG ٓ‫يَّي‬ yamIn 1 ID: SWADESH_200 English word: left Context: I am to the ___ of the boy (Looking at a picture of boys) EA01 1 ENG ‫ِؽّاي‬ ʃimAl 1 EA02 1 ENG ‫ِؽّاي‬ ʃimAl 1 173  GA01 1 ENG ‫يِغاس‬ yisAr 2 GA02 1 ENG ‫يَغاس‬ yasAr 2 LA01 1 ENG ‫يَغاس‬ yasAr 2 LA01 2 ENG ‫ؽّاي‬ ʃmAl 1 LA02 1 ENG ‫ؽّاي‬ ʃmAl 1 MA01 1 ENG ‫ٌيغْش‬ lIsər 2 MA02 1 ENG ‫ٌيغْش‬ lIsər 2 SA01 1 ENG ‫يَغاس‬ yasAr 2 SA01 2 ENG ‫ِؽّاي‬ ʃimAl 1 ID: SWADESH_201 English word: at Context: I will see you ___ the meter where I always park my car EA01 1 ENG ‫ػَٕذ‬ ʕand 1 EA02 1 ENG ‫ػَٕذ‬ ʕand 1 GA01 1 ENG ‫ِػٕذ‬ ʕind 1 GA02 1 ENG ‫ِػٕذ‬ ʕind 1 LA01 1 ENG ‫ِػٕذ‬ ʕind 1 LA02 1 ENG ‫ػَال‬ ʕala 2 MA01 1 ENG ‫ِػٕذ‬ ʕind 1 MA02 1 ENG ‫ِػٕذ‬ ʕind 1 SA01 1 ENG ‫ِػٕذ‬ ʕind 1 ID: SWADESH_202 English word: in Context: He is ___ the city EA01 1 ENG ‫في‬ fi 1 EA02 1 ENG ‫في‬ fi 1 GA01 1 ENG َِ ‫ف‬ fi 1 GA02 1 ENG َِ ‫ف‬ fi 1 LA01 1 ENG َِ ‫ب‬ bi 2 LA02 1 ENG َِ ‫ب‬ bi 2 MA01 1 ENG َْ ‫ف‬ fə 1 MA02 1 ENG َْ ‫ف‬ fə 1 SA01 1 ENG ‫في‬ fI 1 SA01 2 ENG َِ ‫ب‬ bi 2 ID: SWADESH_203 English word: with Context: I am ___ my friends EA01 1 ENG ‫َِغ‬ maʕ 1 174  EA02 1 ENG ‫َِغ‬ maʕ 1 GA01 1 ENG ‫َِ ََغ‬ maʕa 1 GA02 1 ENG ‫ِ ََغ‬ mʕa 1 LA01 1 ENG ‫َِغ‬ maʕ 1 LA02 1 ENG ‫َِغ‬ maʕ 1 MA01 1 ENG ‫ِ ََغ‬ mʕa 1 MA02 1 ENG ‫ِ ََغ‬ mʕa 1 SA01 1 ENG ‫َِ ََغ‬ maʕa 1 ID: SWADESH_204 English word: and Context: Ali ___ Saleh are friends EA01 1 ENG ٚ‫ء‬ ʔu 1 EA02 1 ENG ٚ‫ء‬ ʔu 1 GA01 1 ENG ٚ‫ء‬ ʔu 1 GA02 1 ENG ٚ‫ء‬ ʔu 1 LA01 1 ENG ٚ‫ء‬ ʔu 1 LA02 1 ENG ٚ‫ء‬ ʔu 1 MA01 1 ENG ٚ‫ء‬ ʔu 1 MA02 1 ENG ٚ‫ء‬ ʔu 1 SA01 1 ENG ََٚ wa 1 ID: SWADESH_205 English word: if Context: Let me know ___ you can read this EA01 1 ENG ٌَٛ law 1 EA01 2 VAR ‫ِءصا‬ ʔiza 2 EA02 1 ENG ٌَٛ law 1 EA02 2 VAR ‫ِءصا‬ ʔiza 2 GA01 1 ENG ‫ِءرا‬ ʔiða 2 GA01 2 VAR ٌَٛ law 1 GA02 1 ENG ‫ِءرا‬ ʔiða 2 LA01 1 ENG ٌَٛ law 1 LA01 2 ENG ‫ِءرا‬ ʔiða 2 LA02 1 ENG ‫ِءصا‬ ʔiza 2 MA01 1 ENG ‫ِءال‬ ʔila 3 MA02 1 ENG ‫ِءال‬ ʔila 3 SA01 1 ENG ‫ِءرا‬ ʔiðA 2 SA01 2 ENG ٌَٛ law 1 ID: SWADESH_206 English word: because 175 Context: I went to the clinic ___ I was sick EA01 1 ENG ْ‫ػَؾا‬ ʕaʃAn 1 EA01 2 VAR ْ‫َػٍَؾا‬ ʕalaʃAn 1 EA02 1 ENG ْ‫ػَؾا‬ ʕaʃAn 1 EA02 2 VAR ْ‫َػٍَؾا‬ ʕalaʃAn 1 GA01 1 ENG َْ‫يْ َء‬ ləʔan+a 2 GA01 2 VAR ْ‫ػَؾا‬ ʕaʃAn 1 GA02 1 ENG َْ‫ِي َء‬ liʔan+a 2 GA02 2 VAR ْ‫ػَؾا‬ ʕaʃAn 1 LA01 1 ENG ْ‫ػَؾا‬ ʕaʃAn 1 LA01 2 ENG ْ‫َي ِء‬ laʔin 2 LA02 1 ENG ْ‫َي َء‬ laʔan 2 MA01 1 ENG ‫ٌْسماػ‬ ləħqAʃ 3 MA01 2 VAR ‫زيد‬ ħIt 4 MA02 1 ENG ‫زيد‬ ħIt 4 SA01 1 ENG َْ‫ِي َء‬ liʔan+a 2 ID: SWADESH_207 English word: name Context: The ___ of the boy is Ali EA01 1 ENG ُ‫ِءع‬ ʔism 1 EA02 1 ENG ُ‫ِءع‬ ʔism 1 GA01 1 ENG ُْ‫ِءع‬ ʔisəm 1 GA02 1 ENG ُْ‫ِءع‬ ʔisəm 1 LA01 1 ENG ُْ‫ِءع‬ ʔisəm 1 LA02 1 ENG ُْ‫ِءع‬ ʔisəm 1 MA01 1 ENG ‫عّيد‬ smIt 1 MA02 1 ENG ‫عّيد‬ smIt 1 SA01 1 ENG ُ‫ِءع‬ ʔism 1 176  APPENDIX B ENCODING THE MATHEMATICAL REPRESENTATION OF SOUND Dimension 1: Place of articulation Place of articulation Default value Abbreviation bilabial b 1 labiodental l 0.9 dental d 0.8 alveolar a 0.7 postalveolar e 0.6 palatal p 0.5 central_vowel c 0.4 velar v 0.3 uvular u 0.2 pharyngeal r 0.1 glottal g 0 177 Dimension 2: Degree of constriction at the place of articulation Degree of constriction Default value Abbreviation stop s 0 fricative f 0.2 approximant t 0.4 high vowel h 0.6 mid vowel m 0.8 low vowel w 1 Dimension 3: Voicing Dimension 4: Nasal Dimension 5: Lateral Dimension 6: Trill/Flap Dimension 7: Affricated Dimension 8: Rounded Dimension 9: Long_vowel. Long vowels expressed by upper case letters Dimension 10: Geminated. Expressed by + Dimension 11: Emphatic. Expressed by ˤ 178 The list of phonemes Long_vowel constriction articulation Geminated Affricated Degree of Trill/Flap Emphatic Rounded Place of Voicing Lateral Phone Nasal b b s 1 0 0 0 0 0 0 0 0 t a s 0 0 0 0 0 0 0 0 0 ¢ a s 0 0 0 0 1 0 0 0 0 d a s 1 0 0 0 0 0 0 0 0 ʤ a s 1 0 0 0 1 0 0 0 0 k v s 0 0 0 0 0 0 0 0 0 g v s 1 0 0 0 0 0 0 0 0 q u s 0 0 0 0 0 0 0 0 0 ʔ g s 0 0 0 0 0 0 0 0 0 m b s 1 1 0 0 0 0 0 0 0 n a s 1 1 0 0 0 0 0 0 0 r a s 1 0 0 1 0 0 0 0 0 f l f 0 0 0 0 0 0 0 0 0 θ d f 0 0 0 0 0 0 0 0 0 ð d f 1 0 0 0 0 0 0 0 0 s a f 0 0 0 0 0 0 0 0 0 z a f 1 0 0 0 0 0 0 0 0 ʃ e f 0 0 0 0 0 0 0 0 0 ʒ e f 1 0 0 0 0 0 0 0 0 x v f 0 0 0 0 0 0 0 0 0 ʁ u f 1 0 0 0 0 0 0 0 0 ħ r f 0 0 0 0 0 0 0 0 0 ʕ r f 1 0 0 0 0 0 0 0 0 h g f 0 0 0 0 0 0 0 0 0 y p t 1 0 0 0 0 0 0 0 0 l a t 1 0 1 0 0 0 0 0 0 w v t 1 0 0 0 0 1 0 0 0 i p h 1 0 0 0 0 0 0 0 0 u v h 1 0 0 0 0 1 0 0 0 a c w 1 0 0 0 0 0 0 0 0 ə c m 1 0 0 0 0 0 0 0 0 I p h 1 0 0 0 0 0 1 0 0 U v h 1 0 0 0 0 1 1 0 0 A c w 1 0 0 0 0 0 1 0 0 179