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Course Detail

Course Name Spoken Language Processing
Course Code 15ECE333
Program B. Tech. in Electronics and Communication Engineering
Year Taught 2019


Unit 1

Speech analysis: source filter modeling – Speech sounds – Lip radiation – Linear prediction – Lattice filters – Levisnon-Durbin recursion. Feature extraction for speech processing: Short term Fourier transform – Wavelets – cepstrum – Sinusoidal and harmonic representations – Mel frequency cepstral coefficients (MFCC) – Perceptual linear prediction (PLP) – Mel filter bank energies.

Unit 2

Principles of speech coding: Main characteristics of a speech coder – Key components of a speech coder – From predictive coding to CELP – Improved CELP coders – Wide band speech coding – Audio-visual speech coding. Speech synthesis: Linguistic processing – Acoustic processing – Training models automatically – Text preprocessing – Grapheme to phoneme conversion – Rule based and decision tree approaches – Syntactic prosodic analysis – Prosodic analysis – Speech signal modeling

Unit 3

Principles of speech recognition: Hidden Markov models (HMM) for acoustic modeling, Observation probability and model parameters – HMM as probabilistic automata – Viterbi algorithm – Language models – n-gram language modeling and difficulties with the evaluation of higher order n-grams and solutions. Spoken keyword spotting approaches – Evaluation metric – Spoken language identification – Approaches – Acoustic – Phonotactic – LVCSR based.

Text Books

  1. Joseph Mariani (Ed), “Spoken Language Processing”, John Wiley & Sons, 2009.
  2. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing, A guide to theory, algorithm and system development”, Prentice Hall, Inc, New Joursey, USA, 2001.


  1. Benesty, M MSondhi, Y. Huang (Eds.), “Springer Handbook on Speech Processing”, Springer- Verlag Berlin, Heidenberg, 2008.

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