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