Speech Recognition No Code
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1. The document describes a digital signal processing project that aims to build a program to detect speech signals of the words "yes" or "no" using MATLAB. 2. Data was collected from testing the program on 8 speech samples each of "yes" and "no". The data showed higher frequencies and lower amplitudes for "yes" compared to "no", allowing the program to accurately distinguish between the words 100% of the time. 3. The program works by using a microphone to record speech, applying a fast Fourier transform in MATLAB to analyze frequencies, and outputting "Yes" or "No" depending on whether values are above or below a set threshold.
Speech Recognition No Code
- 1. Gerome Jan Llames MEECE - CCO Project in MEE 1231: Digital Signal Processing Yes or No Speech Recognition Objectives: 1. To build a program, involving Digital Signal Processing, which would detect the speech signal if it is a Yes or a No. 2. To know the difference between a yes and a no signal. INTRODUCTION According to Wikipedia, in Computer Science and Electrical Engineering speech recognition (SR) is the translation of spoken words into text. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT). Some SR systems use "speaker independent speech recognition" while others use "training" where an individual speaker reads sections of text into the SR system. These systems analyze the person's specific voice and use it to fine tune the recognition of that person's speech, resulting in more accurate transcription. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent" systems. Speech recognition applications include voice user interfaces such as voice dialing (e.g. "Call home"), call routing (e.g. "I would like to make a collect call"), domotic appliance control, search (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), speech-to-text processing (e.g., word processors or emails), and aircraft (usually termed Direct Voice Input). Although speech recognition in general is a very complex problem, even a simple program can distinguish between the two words yes and no. Although any two words could be used for a project like this, yes and no were chosen because there are real systems that do exactly this task. For example, calls to a company and telephone surveys are often handled by automated systems that ask the person questions and attempt to determine the response using speech recognition. In a situation where the question is answered by yes or no, a yes/no speech recognition system is very useful.
- 2. DIGITAL SIGNAL PROCESSING SYSTEM System Workflow This project runs in real time. It all starts from the speech signal, yes or no, which makes use the built-in microphone of any computer. Then the signal will be processed in MATLAB where fft takes place. Finally, if the value is below the threshold it would display Yes and no if it’s the other way around.
- 3. DATA ANALYSIS The two table's shows data collected after 8 real time testings. It achieves an impressive 100% accuracy. It shows the voices, frequencies, amplitudes and f values. With these data we can say that there are more signals in the higher frequency in the word yes compared to no while higher amplitude occurs when the word no is spoken. With threshold value at 12, I can say that indeed it has a 100% accuracy. Voice Frequency Amplitude f value YesMine ~ 1400 Hz ~ 0.3 5.9218 YesBaruc ~ 1800 Hz ~ 0.25 1.8685 YesAyn ~ 1600 Hz ~ 0.6 4.2221 YesEra ~ 1400 Hz ~ 1.0 7.2813 YesGeorge ~ 1400 Hz ~ 0.45 5.5708 YesSoheib ~ 600 Hz ~ 0.35 9.6850 YesJaybee ~ 1800 Hz ~ 0.7 3.7257 YesJudilyn ~ 1400 Hz ~ 0.3 5.3822 Table 1: Data analysis of YES Voice Frequency Amplitude f value NoMine ~ 1000 Hz ~ 0.4 21.3383 NoBaruc ~ 1000 Hz ~ 0.4 18.6965 NoAyn ~ 900 Hz ~ 0.7 18.0442 NoEra ~ 1200 Hz ~ 0.4 20.2817 NoGeorge ~ 1000 Hz ~ 0.9 25.5547 NoSoheib ~ 450 Hz ~ 0.28 18.1870 NoJaybee ~ 1000 Hz ~ 0.8 30.7095 NoJudilyn ~ 1600 Hz ~ 0.8 15.3298 Table 2: Data analysis of No
- 4. SCREENSHOTS OF THE DATA IN TABLE 1 & TABLE 2 YesMine: YesBaruc:
- 9. HOW TO RUN THE PROGRAM? Step 1: Run the YesOrNoRecorder m-file. "YesOrNoRecorder" Step 2: Run the Yes_Or_No_Project function file with a sampling frequency of 44100 Hz which is twice the frequency of the speech signal. "Yes_Or_No_Project(x,44100)" Watch the video here: http://www.youtube.com/watch?v=EOcp7pxQOBA&feature=youtu.be LIMITATIONS There was no external microphone that was used. I used the built in microphone in my laptop. Before I started the recording, it was make sure that there were no unnecessary loud noises. Using a filtered microphone would be a good choice. CONCLUSION Yes or No Speech recognition is a good example of a system which involves Digital Signal Processing. It make use of the well-known Fast Fourier Transform (fft). Before I started making the project, I already found some interesting facts about the two words yes and no. The two words have an unvoiced consonant and voiced consonant sound respectively. Voiced consonant means that when we say a certain word there is that vibration in our vocal cords while unvoiced consonant doesn't. Unvoiced consonants has that larger energy compared to that of Voiced as well. That was proven when the testing was conducted. The data that was gathered after the testing shows the difference between the word yes and no. In the waveform x that was plotted, there were more values in the high frequency in the yes signals than that of no. It was because of the sound 's'. However the amplitude of the no signals were much higher compared to that of yes. Finally, based on the data, the objective was successfully achieved with an excellent 100% accuracy.