G010424248

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 4, Ver. II (Jul - Aug .2015), PP 42-48
www.iosrjournals.org
DOI: 10.9790/2834-10424248 www.iosrjournals.org 42 | Page
Analysis of Speech Coding Algorithms for Hindi Language
Sukriti Sharma1
, Charu2
1, 2
(Department of Electronics and Communication, Manav Rachna College of Engineering, Faridabad, India)
Abstract: Speech coding is an algorithm used to analyze the special, non- stationary and intelligent speech
signal in order to extract its important parameters and to compress it for the maximum utilization of available
bandwidth. To achieve this, various speech coding algorithms have been effectively used. Out all these
algorithms, Linear Predictive Coding (LPC) is the most powerful one as it provides accurate estimation of
speech parameters and is computationally effective and used to represent the speech signal at reduced bit rates
while preserving the quality of the signal. Voice- excited LPC is the algorithm proposed in this paper. This
algorithm has been implemented using Hindi and English male and female voices and trade- offs between bit
rates, delay, power signal to noise ratio and complexity are analyzed. It results in low bit-rates and better signal
to noise ratio.
Keywords: Bit-Rates, Discrete Cosine Transform, Hindi and English Speech Signal, Linear Predictive Coding,
Power Signal to Noise Ratio.
I. Introduction
Digital transmission is used to provide more flexibility, reliability, privacy, security and cost
effectiveness. Due to these reasons, there is a continuous need of digital transmission today in many applications
like satellite, radio and storage media like CD ROMS and silicon memory. But now, these applications are band
limited. Thus, it is required to reduce the number of bits of transmitted signal.
Speech coding is still a major subject in the area of digital speech processing in which the speech
signals are analyzed in order to obtain its important parameters and to compress it to make maximum utilization
of available bandwidth. But note that compression of speech signal should be such that it does not harm the
intelligibility and quality of transmitted speech signal. To accomplish speech coding practically, number of
voice coders or vocoders are employed which can be classified into three: waveform coders, source coders and
hybrid coders. Waveform coders operate at high bit rates which lead to very good quality speech. Source coders
operate at very low bit rates and reconstructed speech is „robotic‟ sounding. Hybrid coders use elements of both
waveform and source coders and produces good reconstructed speech at average bit rates. [1]
The vocoder employed here is a source vocoder- modified version of LPC-10. This speech coder is
analyzed using subjective and objective analysis. Subjective analysis includes listening of encoded Hindi and
English speech signals and making the judgment of its quality which will depend on the opinion of the listener.
Objective analysis includes computation of power signal to noise ratio between original and encoded Hindi and
English speech signals which will be included within the performance analysis. [2]
II. Technical Approach
The complete cycle of speech production in humans can be summarized as air is pushed up from the
lungs through vocal tract and is up through mouth to generate speech as shown in Fig. 1. The air flow from the
lungs is called the excitation signal which causes the vocal cords to vibrate which play major role in shaping the
sound produced. In technical terms, lungs acts as a source of the speech and vocal tract as a filter that produces
different types of sounds that in turn forms a speech.
Fig. 1. Physical Model of Speech Production in Humans.

This human speech production model is the model which is used in LPC. The idea behind it is
separating source from filter during production of sound and this model is used in both analysis and synthesis
part of LPC and is derived from mathematical approximation of vocal tract produced as shown in Fig. 2. The air
travelling through vocal tract is the source which can be either periodic for voiced sound produced and random
for unvoiced sounds.
Fig. 2. Human vs. Voice Coder Speech Production.
II.I LPC Model Implementation
The speech signals are analyzed and synthesized using LPC technique which is the method used to
estimate the basic parameters like pitch, formants and spectra of input speech signal. The block diagram of LPC
vocoder is shown by Fig. 3.
Fig. 3. Block Diagram of LPC Vocoder
II.I.I Sampling
The speech signal is sampled at an appropriate frequency to capture all the necessary frequency
components needed for speech processing and recognition. 10 kHz is typically the sampling frequency as most
of the speech energy is included in frequencies below 4 kHz (but some women and children violate from this
fact).
II.I.II Segmentation
Properties of speech signal change with time. Thus, to process effectively, it is necessary to work frame
by frame for which speech is segmented into blocks. The length of the blocks in LPC analysis is between 10ms
and 30 ms as within this small interval, the speech signal remains roughly constant.
II.I.III Pre- emphasis
The spectral envelope of speech signal has high frequency roll off due to radiations of sound from lips
and these high frequency components have low amplitude that increases the dynamic range of speech spectrum.
The speech signal is processed using time- varying digital filter, defined by equation (1).
(1)
The filter described in (1) is a pre-emphasis filter which is used to boost the high frequencies in order to flatten
the spectrum. Denoting x[n] as input to filter and y[n] as output, the difference equation (2) is applied.
(2)
Value of α is near 0.9. To maintain same spectral shape for synthetic speech, it is filtered by de-emphasis filter,
defined by equation (3), whose system function is the inverse of pre-emphasis filter.
(3)
LPC ANALYZER
PITCH DETECTOR
CODER
CHANNEL
INPUT
SPEECH

II.I.IV Voice detector
The purpose of voicing detector is to determine which frame is voiced or unvoiced. Voice detector is
one of the most critical components of LPC coder as misclassification of voicing will result in disastrous
consequences on the quality of synthetic speech. A simple voicing detector can be implemented by employing
„Zero Crossing Rate (ZCR)‟ technique in which if rate is lower than a certain threshold then the frame is
considered out to be voiced else unvoiced. ZCR of frame ending at time instant, m is given by equation (4).
(4)
where, sgn (.) is the sign function returning ±1 depending on the operand.
II.I.V Pitch estimation
Pitch or fundamental frequency is one of the most important parameters of speech analysis. Here,
autocorrelation function is employed to estimate correct pitch period for voiced or unvoiced frames. If frame is
unvoiced then white noise is used with pitch period, T=0 and if frame is voiced, impulse train with finite pitch
period, T becomes the excitation of LPC filter as represented by Fig. 4.
Fig.4. Mathematical Model of Speech Production
II.I.VI Coefficient determination
The prediction coefficients which can be estimated by minimizing the mean square error between the
reconstructed and the original speech signal using equations (1) and (2). For efficient estimation, Levinson-
Durbin Recursion algorithm is employed.
II.I.VII Gain calculation
For unvoiced case, prediction error is given by equation (5).
(5)
Where, N as the length of frame
For voiced case, prediction error is given by equation (6).
(6)
And N is assumed to be N > T.
For unvoiced case, gain (G) is given by equation (7).
(7)
For voiced case, the impulse train power having amplitude of G and pitch period, T and interval of
[N/T] T must be equal to p.
II.I.VIII Quantization
Usually, direct Quantization of the predictor coefficients is not employed. To ensure stability of the
coefficients (the poles and zeros must lie within the unit circle in the z-plane) a relatively high accuracy (8-10
bits per coefficients) is needed. This comes from the effect that small changes in the predictor coefficients lead
to relatively large changes in the pole positions. There are two possible alternatives. One of them is the partial
reflection coefficients (PARCOR). These are intermediate values during the calculation of the well-known
Levinson-Durbin recursion. Quantizing the intermediate values, Line Spectral Frequencies (LSFs) is less
problematic than quantifying the predictor coefficients directly as LSFs are less sensitive to quantization noise
that ensures more stability. Thus, a necessary and sufficient condition for the PARCOR values is .

II.II Voice- Excited LPC Vocoder
To improve the quality of sound, voice-excited LPC vocoder is employed. Fig. 5. represents the block
diagram of voice-excited LPC vocoder. [4] Its main difference to plain LPC is use of excitation detector instead
of pitch detector in plain LPC.
The main purpose behind voice-excited LPC is to avoid the detection of pitch and use of impulse train
for synthesizing the speech. Instead, it is better to estimate the excitation signal. As a result, input signal is
filtered with the estimated system function of LPC analyser. The filtered signal thus obtained is called residual
signal which when transmitted to the receiver will result in good quality. Also, high compression rates can be
achieved by computing discrete cosine transform (DCT) of residual signal in which the most of the energy is
contained in first few coefficients.
Fig. 5. Voice-Excited LPC Vocoder
III. Comparison Between Hindi and English Speech Signals
All the Indian languages have natural languages that share several features and sounds with the other
languages of the world as one cannot expect a language or a group of languages entirely composed of speech
sounds that cannot be found anywhere else. Presence or absence of voicing in a speech sound gives rise to
distinction of voiced- unvoiced sounds. This basic distinction that is found in English speech signal is employed
in Hindi speech signal to a great extent.
Languages differ by the „amount‟ of voicing that is present in it. English voiced plosives are considered
to be „partially‟ voiced as compared to „fully‟ voiced plosives. On the other hand, in an Indian language such as
Urdu or Hindi, release aspiration does not play a key role in distinguishing unvoiced and voiced plosives. The
reason is that these languages maintain a contrast between unvoiced aspirated and unaspirated plosives, whereas
English does not have such a contrast. Hindi speech signal utilizes the feature of aspiration to separate their
unvoiced aspirates from their unvoiced unaspirates, whereas English speech signal uses the same feature of
aspiration to separate its voiced from voiceless plosives. The quality of Voiced sounds in Hindi speech signal is
of „modal‟ variety. Modal voice is generated by regular vibrations of the vocal folds at any frequency within the
speaker‟s normal range.
Pitch is the fundamental frequency and an important parameter of speech coding. All natural languages
use relative variations in pitch to bring out intonational differences like differences between interrogative and
declarative sentences or emotional and attitudinal differences on the part of the speaker.
As compared to consonants, vowels of Hindi speech signal do not have those significant different
features. Hindi speech signal is supposed to have syllable-timed rhythm whereas English speech signal has a
stress-timed. As stress does not have any phonemic value in Indian languages, it does not control the quality as
well as the quantity of vowels in a word. Thus, Hindi speech signal does not exhibit drastic changes in the
quantity and quality of a vowel which usually depends upon the syllabic stress.
IV. Mean Square Error
The difference between the original and reconstructed speech signal is computed which is called error
signal, denoted by „err‟ and mean square error (MSE) is computed by taking the average of squares of sample
values of err. The value of MSE should be as low as possible and is given by equation (3):
(8)
TABLE I shows the comparison of both Hindi and English Speech Signals in terms of MSE for both
Plain LPC and Voice- Excited LPC and it reflects that MSE of English speech signal is more than Hindi speech
signal in both LPC algorithms.

Table. I Comparison of MSE for Plain LPC and Voice- Excited LPC using Hindi
And English Speech Signals.
Vocoder Type Hindi Speech Signal English Speech
Signal
Plain LPC 1.0529 1.3623
Voice- Excited LPC 0.00414 0.0051
V. Performance Analysis
Fig. 6. represents the waveforms of Hindi speech signal “मेरा नाम सुकृ ति शमाा है, मैं एमटेक ईसीई की
छात्रा हॉ|” and Fig. 7. represents the waveforms of English speech signal “My name is Sukriti Sharma, I am from
M.Tech ECE” with number of samples in x-axis versus amplitude in y-axis resulted by implementing both of
the LPC techniques in MATLAB R20013a.
Fig. 6.Waveforms of Hindi speech signal (a) Original speech signal, (b) Plain LPC reconstructed
Speech signal and (c) Voice-excited LPC reconstructed speech signal.
Fig. 7.Waveforms of English speech signal (a) Original speech signal, (b) Plain LPC reconstructed
Speech signal and (c) Voice-excited LPC reconstructed speech signal.
Performance analysis is done with subjective and objective analysis where the original Hindi and
English speech signals are compared with the plain LPC and voice-excited LPC reconstructed speech signals. In
both the cases, Subjective analysis shows that the reconstructed Hindi and English speech signals have lower
quality than original speech signal. The plain LPC reconstructed speech signal has low pitch and sound seems to
be whispered. But, the reconstructed speech signal of voice-excited LPC appears to be more spoken; less

whispered and appears closer to original speech signal. On other hand, the objective analysis includes following
mentioned parameters.
V.I Bit Rates
Bit rates in both the cases are lower than the original speech signal as shown by TABLE II and TABEL
III. Here, following parameters are employed:
 Sampling rate Fs = 16000 Hz (or samples/sec.).
 Window length (frame): 20 ms which results in 320 samples per frame by the given sampling rate Fs.
 Overlapping: 10 ms, hence: the actual window length is 30ms or consists of 480 samples.
 There are 50 frames per second.
 Number of predictor coefficients of the LPC model = 10.
Table. II Bit Rates for Plain LPC
Parameters Number of bits per frame
Predictor coefficients 10 bits k1 and k2 (5 each),10 bits k3
and k4 (5 each),16 bits k5, k6, k7, k8
(4 each), 3 bits k9,2 bits k10
Gain 5
Pitch period 6
Voiced/unvoiced switch 1
Synchronization 1
Total 54
Overall bit rate (54bits/frame)*(50frames/second)=
2700 bits/second
Table. III Bit Rates for Voice- Excited LPC
Parameters Number of bits per frame
Predictor coefficients 10 bits k1 and k2 (5 each),10 bits k3
and k4 (5 each),16 bits k5, k6, k7, k8
(4 each), 3 bits k9,2 bits k10
Gain 5
DCT coefficients 40*4
Synchronization 1
Total 207
Overall bit rate (207bits/frame)*(50frames/second)
= 10350 bits/second
Thus, it is clear that voice-excited LPC needs more than twice the bandwidth needed in plain LPC. This
bandwidth increase results in better sound but still not perfect. [5]
V.II Computational complexity
In voice-excited LPC, autocorrelation employed in Plain LPC is omitted and instead DCT and its
inverse are employed. But the total number of operations per frame are more in voice-excited than that of Plain
LPC. Thus, the improved quality needs higher number of FLOPS (Floating-point Operations per Second). [6]
V.III Power Signal to Noise Ratio
It is given by equation (4).
(9)
In equation (4), A is the number of samples of original speech signal. It is found that PSNR of plain
LPC using both Hindi and English speech signals is negative that means it is noisier and noise is much stronger
than the original signal but for voice-excited LPC, PSNR for both the signals is positive that means it is better
but still does not sounds exactly like original speech signal. This is represented by TABLE IV.
Table. IV Comparison of PSNR for Plain LPC and Voice- Excited LPC
Using Hindi and English Speech Signals.
Vocoder Type Hindi Speech Signal English Speech Signal
Plain LPC -5.8592 -2.6750
Voice- Excited LPC 18.1933 21.5750

VI. Conclusion
Speech coding algorithms have been analyzed using two LPC algorithms: Plain LPC and Voice-
excited LPC on Hindi and English languages. It has been found that the results obtained from Voice-excited
LPC using English speech signal are more intelligible as compared to Hindi speech signal whereas from Plain
LPC, the results are poor and barely intelligible for both English and Hindi speech signals. But through Voice-
excited LPC, the improved quality of compressed reconstructed speech signal requires more number of bits per
frame that leads to increased bandwidth requirement. Also, SNR for both the algorithms using Hindi and
English Speech Signals were computed and compared and it has been found that sound of reconstructed speech
signal due to Plain LPC has negative SNR for each language that results in noisy and whispered sound. On the
other hand, Voice-excited LPC has far better sound and positive SNR for both Hindi and English speech signals.
Since, the voice-excited LPC gives pretty good results with all the required limitations, and we can try to
improve it. A major improvement can be the compression of the errors. If we send them in a lossless manner to
the synthesizer, the reconstruction would be perfect.
References
[1]. L.R.Rabiner and R. W. Schafer, “Theory and Application of Digital Speech Processing Preliminary Edition”.
[2]. The newest breeds trade off speed, energy consumption, and cost to vie for an ever bigger piece of the action. BY JENNIFER
EYRE Berkeley Design Technology Inc.
[3]. B. S. Atal, M. R. Schroeder, and V. Stover, “Voice- Excited Predictive Coding System for Low Bit-Rate Transmission of
Speech”, Proc. ICC, pp.30-37 to 30-40, 1975.
[4]. M. H Johnson and A. Alwan, “Speech Coding: Fundamentals and Applications”, to appear as a chapter in the encyclopedia of
telecommunications, Wiley, December 2002.
[5]. Sukriti Sharma, Charu, “Lossless Linear Predictive Coding For Speech Signals”, International Journal of Science, Technology &
Management, Volume No 04, Special Issue No. 01, March 2015, ISSN (online): 2394-1537.
[6]. Orsak, G.C rt al, “Collaborative SP education using the internet and MATLAB” IEEE Signal Processing Magazine, Nov, 2009 vol
12, no6, pp 23-32.
[7]. H. Huang, H. Shu, and R. Yu, “Lossless Audio Compression In The New IEEE Standard For Advanced Audio Coding”, IEEE
International Conference on Acoustic, Speech and Signal Processing (ICASSP) 2014, pp 6984-6988.

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