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6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts.
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Description
Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing.
Course content
- Course Overview Unlimited
- Acoustic Theory of Speech Production Unlimited
- Speech Sounds Unlimited
- Signal Representation Unlimited
- Vector Quantization Unlimited
- Pattern Classification (1) Unlimited
- Pattern Classification (2) Unlimited
- Search Unlimited
- Hidden Markov Modeling (1) Unlimited
- Language Modeling Unlimited
- Guest Lecture by Karen Livescu: Graphical Models Unlimited
- Guest Lecture by Rita Singh: Hidden Markov Modeling (2) Unlimited
- Guest Lecture by Rita Singh: Hidden Markov Modeling (3) Unlimited
- Segment-Based ASR Unlimited
- Guest Lecture by Lee Hetherington: Finite-State Transducers Unlimited
- Acoustic-Phonetic Modeling Unlimited
- Robust ASR (1) Unlimited
- Guest Lecture by Timothy Hazen: Robust ASR (2) Unlimited
- Guest Lecture by Timothy Hazen: Adaptation Unlimited
- Guest Lecture by Timothy Hazen: Paralinguistic Information Unlimited
Instructor
