CSVTU BE VII Semester ET&T Speech Signal Processing Syllabus

Chhattisgarh Swami Vivekanand Technical University, Bhilai

Semester : VII Branch: Electronics & Telecommunication

Subject: Speech Signal Processing

Total Theory Periods: 40 Total Tutorial Periods: 12

Total Marks in End Semester Examination: 80

Minimum number of Class tests to be conducted: Two


Speech: Production, Perception and Acoustic-Phonetic Characterization:


Introduction, Speechproduction

process, Time and frequency domain representation of speech, Speech sounds and features, The


vowels, Diphthongs, Semivowels, Nasal Consonants, Unvoiced Fricatives, Voiced Fricatives, Voiced &


Unvoiced Stops, Acoustic-Phonetic Approach to Speech Recognition, Statistical Pattern-Recognition Approach


to Speech Recognition, AI Approaches to Speech Recognition, Neural Networks and their Application to


Speech Recognition.



Spectral Analysis of Speech:


Short time Fourier analysis, filter bank design, speech coding, subband coding

of speech, transform coding, channel vocoder, formant vocoder, cepstral vocoder, vector quantizer coder.



Speech Synthesis:


Pitch extraction algorithms, Gold Rabiner pitch trackers, autocorrelation pitch trackers,

voice/unvoiced detection, homomorphic speech processing, homomorphic systems for convolution, complex


cepstrums, pitch extraction using homomorphic speech processing



Automatic speech recognition systems:


Isolated word recognition, connected word recognition, large

vocabulary word recognition systems, pattern classification, DTW, HMM, speaker recognition systems,


speaker verification systems, speaker identification systems.



Hidden Markov Models:


Discrete-Time Markov Processes, Extensions to HMMs, Coin-toss Models, The Urnand-

Ball Model, Elements of an HMM, HMM generator of observations. Three Basic problems for HMMs and


their solutions, Probability Evaluation, ‘Optimal’ State sequence, Parameter estimation, Re-estimation


procedure. HMM types, continuous observation densities in HMMs, Autoregressive HMMs, Variants on HMM


structures, Inclusion of Explicit State Duration Density in HMMs, Optimization Criterion – ML, MMI and MDI,


Comparisons of HMMs.


Name of Text Books:

1. Fundamentals of Speech Recognition, Rabiner L. and Juang B., Pearson Education



Owens F.J., ‘‘Signal Processing of Speech’’, Macmillan New Electronics


Names of Reference Books:

1. Speech and Language Processing, Jurafsky, Pearson Education

2. Discrete Time Speech Signal processing: Principles and Practice, Quatieri, Pearson Education

3. Saito S. & Nakata K., “Fundamentals of Speech Signal Processing”, Academic Press

4. Thomas Parsons, “Voice and Speech Processing”, McGraw Hill Series

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