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BSEE4001 - Speech Technology
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BSEE4001 - Speech Technology
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| Field | Value |
|---|---|
| Course Code | BSEE4001 |
| Level | Degree Level Course |
| Credits | 4 |
| Type | Elective |
| Pre-requisites | None |
| Videos | YouTube Playlist |
📖 Description
The following are the suggested books for the course:
🗓️ Weekly Syllabus
| Week | Topic |
|---|---|
| Week 1 | Review of Signals and Systems, Continuous time signals and transforms |
| Discrete time signals, Discrete Fourier transform, Autocorrelation and Cross-Cor | |
| Week 2 | Acoustic Feature Analysis of Speech Signals I, II |
| Gaussian mixture models (GMM), universal background model (UBM-GMM), singular value decomposition (S | |
| Week 3 | Hidden Markov model (HMM), Examples of HMM based approach for ASR, TTS, speaker diarization |
| Information bottleneck (IB) based clustering for diarizati | |
| Week 4 | Introduction and History of ASR and TTS |
| Components of ASR: Acoustic Modelling, Punctuation Model (Lexicon) and language modelling (N-Gram Language mod | |
| Week 5 | HMMs for Acoustic Modelling - Monophone, Triphone |
| Speech Synthesis: unit selection, statistical parametric synthesis (HTS) | |
| Week 6 | Neural networks for building speech technologies |
| NN for Acoustic Modelling - Hybrid modelling- Hybrid-NN: DNN,CNN,TDNN | |
| Week 7 | End-to-End Approaches I: |
| CTC, Encoder-decoder Architecture E2E with RNN | |
| Week 8 | Applications to ASR and TTS |
| End-to-End Approaches II | |
| Week 9 | Encoder-decoder Architecture E2E with transformers for ASR and TTS |
| Interesting Problems | |
| Week 10 | Speaker recognition/verification: with ivector, xvector |
| Speaker diarization: using x-vector | |
| Week 11 | Speaker adaptation: (revisit i, x vectors) and introduce s-vectors. |
| Code Switched Speech recognition; Speech Translation | |
| Week 12 | Singing voice synthesis; voice conversion; generic voice synthesis |
📚 Books & Resources
Prescribed Books
The following are the suggested books for the course:
L R Rabiner and R W Schafer, "Theory and Application of Digital Speech
Processing", PH, Pearson, 2011.
L R Rabiner, B-H Juang and B Yegnanarayana, "Fundamentals of Speech
Recognition", Pearson, 2009 (Indian subcontinent adaptation).
Xuedong Huang, Alex Acero, Hsiao-wuen Hon, "Spoken Language Processing: A
guide to Theory, Algorithm, and System Development", Prentice Hall PTR, 2001.
References: Thomas Quatieri, "Discrete-time Speech Processing: Principles and Practice", PH,
2001.
Rabiner and Schafer, "Digital Processing of Speech Signals", Pearson Education,
1993.
Recent research papers
📝 About the Instructors
Prof. S. Umesh
Professor,
Department of Electrical Engineering,
Indian Institute of Technology,
IIT Madras
S. Umesh is a Professor of Electrical Engineering at IIT-Madras. He completed his PhD from the University of Rhode Island,USA and his PostDoctoral Fellowship from the City University of New York. He has also been a visiting researcher at AT&T Research Laboratories, USA; at Machine Intelligence Laboratory Cambridge University Engineering Department, UK and the Department of Computer Science, RWTH-Aachen, Germany.
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He is a recipient of the AICTE Career Award for Young Teachers in 1997 and the Alexander von Humboldt Research Fellowship in 2004. During his stint at Cambridge University in 2004, he was part of the U.S. DARPA's Effective, Affordable Reusable Speech-to-text (EARS) programme. Similarly in 2005 he was part of the RWTH-Aachen's TC-STAR project for transcription of speech from European Parliament's Plenary Sessions. Between 2010-2016, he led a multi-institution consortium to develop ASR systems in Indian languages in the agriculture domain which was funded by MeiTY. He is currently leading the ASR efforts for the Natural Language Translation Mission managed by the Office of Principal Scientific Adviser of Govt. of India.
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Prof. Hema A Murthy
Professor,
Department of Computer Science and Engineering,
IIT Madras
Faculty at the Department of Computer Science and Engineering, Indian Institute of Technology Madras.
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