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Automatic Speech Recognition Spring 2003

chp_speechcomponents

Course Highlights

Major components in a speech recognition system.

6.345 is a course in the department’s “Bioelectrical Engineering” concentration. This course offers a full set of lecture slides with accompanying speech samples, as well as homework assignments and other materials used in the course.

Course Description

6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. 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.

Special Features

Lecture Notes

Media player software, such as Quicktime® PlayerRealOne™ Player, or Windows Media® Player, is required to run the .wav files in this section.
This section contains a complete set of lecture slides for the course, including guest lectures. Lectures 3, 4, and 6 have audio links to speech samples presented during the lectures. 

WEEK # LEC # TOPICS
1 1
2
Course Overview (PDF)
Acoustic Theory of Speech Production (PDF – 1.4 MB)
2 3
4
Speech Sounds (PDF – 3.6 MB)
Speech Sounds (continued)
3 5
6
Signal Representation (PDF – 1.9 MB)
Vector Quantization (PDF – 1.8 MB)
4 7
8
Pattern Classification (1) (PDF – 1.1 MB)
Pattern Classification (2) (PDF)
5 9
10
Search (PDF)
Hidden Markov Modeling (1) (PDF)
6 11
12
Language Modeling (PDF)
Language Modeling (continued)
7 13 Guest Lecture by Karen Livescu: Graphical Models (PDF)
Quiz 1
8 14
15
Guest Lecture by Rita Singh: Hidden Markov Modeling (2) (PDF – 2.1 MB)
Guest Lecture by Rita Singh: Hidden Markov Modeling (3) (PDF – 1.4 MB)
9 16
17
Segment-Based ASR (PDF)
Guest Lecture by Lee Hetherington: Finite-State Transducers (PDF)
10 18
19
Acoustic-Phonetic Modeling (PDF)
Robust ASR (1) (PDF)
11 20
21
Guest Lecture by Timothy Hazen: Robust ASR (2) (PDF)
Guest Lecture by Timothy Hazen: Adaptation (PDF)
12 22
23
Speech Understanding (PDF – 1.1 MB)
Guest Lecture by Timothy Hazen: Paralinguistic Information (PDF – 1.0 MB)
13 Quiz 2
No Lecture
14 Term Project Presentations

Assignments

This section contains a complete set of assignments for the course. The supporting materials supplement the assignments.

ASSIGNMENT # ASSIGNMENT TOPICS SUPPORTING MATERIALS
1 Acoustic Theory (PDF) An Introduction to LAMINAR (PDF)
An Introduction to Using WAVES+ (PDF)
2 Speech Sounds (PDF)
3 Signal Representation (PDF)
4 Acoustic Modeling (PDF)
5 Hidden Markov Models 1 (PDF) Supplement to Q9 and Q10 (PDF)
6 Language Modeling (PDF)
7 Graphical Models (PDF) Suggested Readings (PDF)
8 Hidden Markov Models 2 (PDF)
9 Segment-Based ASR (PDF)
Assignment Errata (PDF)
Source:

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