Speech Signal Processing
Fall 2003
Tuesdays, 9:10 ~12:00 AMInstructor: Berlin Chen
Topic List and Schedule:
|
Date |
Topic | Homework / Project |
|
9/9 |
Course Overview & Introduction | |
| 9/16 |
Spoken Language Structure |
Homework-1:Depict a spectrogram of
a speech utterance with your own name pronounced. (Due: 9/30) (Please observe the formants and harmonics of the fundamental frequency) See Results |
| 9/23 |
Hidden
Markov Models (I) |
Homework-2:
Solving the Problems 1* and 2** for HMM (Due:
10/15) ( *Problem 1 should be solved with Forward Algorithm and Backward Algorithm, respectively. **Problem 2 should be solved with Viterbi Algorithm in both forward and backward directions.) |
|
9/30 |
Hidden
Markov Models (II) |
Homework-3: Solving the Problem 3 for HMM (Baum-Welch Training) (Due: 10/28) |
| 10/7 |
Hidden
Markov Models (III) - Expectation Maximization (EM) Algorithm - Review of Estimation Theory |
|
| 10/14 |
Review
of Digital Signal Processing |
|
|
10/21 |
Review
of Digital Signal Processing Speech Signal Representations |
Project-1:
Small-Vocabulary, Isolated Word Recognition (Due
11/10) |
|
10/28 |
Midterm |
|
|
11/4 |
Speech Signal Representations Linear Prediction Coding of Speech Signals |
Project-2:
linear prediction
coding (Due 11/28) |
|
11/11 |
Linear Prediction Coding of Speech Signals Language Modeling (I) |
|
|
11/18 |
Language Modeling
(I) Acoustic Modeling (I): |
|
|
11/25 |
Acoustic Modeling (II): Cambridge Hidden Markov Model Toolkit(HTK) |
Homework 4: Exercises on HTK Toolkit (Due 12/2)
|
| 12/2 |
Acoustic Modeling
(I): Triphone Modeling, CART etc. Search Algorithms |
Homework 5: Derive the equations of likelihood gains used for data splitting, on P. 179-180 of the textbook (Due 12/9) |
|
12/9 |
Invited
Speaker: Roger Kuo (郭人瑋) Acoustic Modeling (III): Adaptation Techniques for Acoustic Models |
|
|
12/16 |
Invited
Speaker: Louis Tasi (蔡文鴻) Language Modeling (II): SRI Language Modeling Libraries and Tools Language Modeling (III): Adaptation Techniques for Language Models |
|
|
12/23 |
Search
Algorithms Large Vocabulary Continuous Speech Recognition (LVCSR) |
|
|
12/30 |
Robustness
Techniques for Feature Extraction |
|
|
1/6 |
Final Exam |
|
| Discriminant Feature
Extraction and Dimension Reduction Spoken Dialogue Techniques |
Textbook:
1. X. Huang, A. Acero, H. Hon, “Spoken Language Processing,” Prentice Hall, 2001 (全華代理)References:
Books:
1. T. F. Quatieri,“Discrete-Time Speech Signal Processing - Principles and Practice,” Prentice Hall, 2002
2. J. R. Deller, J. H. L. Hansen, J. G. Proakis, “Discrete-Time Processing of Speech Signals,” IEEE Press, 2000
3. F. Jelinek, "Statistical Methods for Speech Recognition," The MIT Press, 1999
4. S. Young et al., “The HTK Book”, Version 3.2, 2002. "http://htk.eng.cam.ac.uk"
5. L. Rabiner, B.H. Juang, “Fundamentals of Speech Recognition”, Prentice Hall, 1993Papers:
1. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech
Recognition,” Proceedings of the IEEE, vol. 77, No. 2, February 1989
2. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm,"
J. Royal Star. Soc., Series B, vol. 39, pp. 1-38, 1977
3. Jeff A. Bilmes "A Gentle Tutorial of the EM Algorithm and its Application to Parameter
Estimation for Gaussian Mixture and Hidden Markov Models," U.C. Berkeley TR-97-021
4. J. W. Picone, “Signal modeling techniques in speech recognition,” proceedings of the
IEEE, September 1993, pp. 1215-1247
5. R. Rosenfeld, ”Two Decades of Statistical Language Modeling: Where Do We Go from
Here?,” Proceedings of IEEE, August, 2000
6. Hermann Ney, “Progress in Dynamic Programming Search for LVCSR,” Proceedings of the IEEE, August 2000
7. "Progress in Dynamic Programming Search for LVCSR", Proceedings of the IEEE, 88(8), August 2000.
8. H. Hermansky, "Should Recognizers Have Ears?", Speech Communication, 25(1-3), 1998.