Speech Recognition

Fall 2012
9:10 ~12:00 am, Mondays
Instructor: Dr.
Berlin Chen (陳柏琳)

Topic List and Schedule:

09/10 Course Overview & Introduction Reading: The Speech Recognition Problem, Chapter 1 of the book  "Statistical Methods for Speech Recognition"
09/17   Hidden Markov Models for Speech Recognition Reading: L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, vol. 77, No. 2, February 1989
09/24   Hidden Markov Models for Speech Recognition HW#1: The first two problems of the HMM modeling (Due: 10/15)
10/01   Review of Probability Axioms and Laws HW#2: The third problem of the HMM modeling (Due: 10/23)
10/08   Maximum Likelihood Estimation  
10/15   Spoken Language Structure HW#3: Isolated Word Recognition (Explanation)
10/22   Acoustic Modeling  
10/29   Language Modeling  
11/05   Language Modeling  
11/12   Midterm  
11/19   Introduction to Hidden Markov Toolkit (HTK) HW#4: HTK Exercise
11/26   Search Algorithm  
12/03   Search Algorithm  
12/10   Speech Signal Representations  
12/17   Large Vocabulary Continuous Speech Recognition (LVCSR)  
12/24   Robustness  
12/31   New Year's Holidays  
1/07   Paper Presentations  
    Topic Language Models and their Applications  

Reference Books:

§   L. Rabiner, R. Schafer, Theory and Applications of Digital Speech Processing, Pearson, 2011  
§   X. Huang, A. Acero, H. Hon, Spoken Language Processing: A Guide to Theory, Algorithm and System Development, Prentice Hall, 2001  
§   Jacob Benesty (ed.), M. Mohan Sondhi (ed.), Yiteng Huang (ed.), Springer Handbook of Speech Processing, Springer, 2007  
§   L. Rabiner, B.H. Juang, “Fundamentals of Speech Recognition”, Prentice Hall, 1993  
§   M.J.F. Gales and S.J. Young. The Application of Hidden Markov Models in Speech Recognition. Foundations and Trends in Signal Processing, 2008  
§   L. Rabiner and R.W. Schafer. Introduction to Digital Speech Processing. Foundations and Trends in Signal Processing, 2007  
§   W. Chou,. B.H. Juang. Pattern Recognition in Speech and Language Processing. CRC Press, 2003  
§   S. Young et al., “The HTK Book”, Version 3.2, 2002. "http://htk.eng.cam.ac.uk"  
§   T. F. Quatieri,“Discrete-Time Speech Signal Processing - Principles and Practice,” Prentice Hall, 2002  
§   F. Jelinek, "Statistical Methods for Speech Recognition," The MIT Press, 1999  
§   J. R. Deller, J. H. L. Hansen, J. G. Proakis, “Discrete-Time Processing of Speech Signals,” IEEE Press, 2000  
§   C. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999  
§   J. Bellegarda, Latent Semantic Mapping: Principles & Applications (Synthesis Lectures on Speech and Audio Processing), 2008  
§   T. K. Landauer, D. S. McNamara, S. Dennis, W. Kintsch (eds.) , Handbook of Latent Semantic Analysis, Lawrence Erlbaum, 2007  
§   Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2004  
§   D. P. Bertsekas, J. N. Tsitsiklis, “Introduction to Probability,” Athena Scientific, 2002  

  Reference Papers:

§   L. Rabiner. The Power of Speech. Science, Vol. 301, pp. 1494-1495, Sep. 2003.  
§   J.M. Baker et al., Research Developments and directions in speech recognition and understanding, part 1, IEEE Signal Processing Magazine 25(3), May 2009.  
§   J.M. Baker et al., Research Developments and directions in speech recognition and understanding, part 2, IEEE Signal Processing Magazine 25(4), July 2009.  
§   J. Schalkwyk et al., "Google Search by Voice: A case study," 2010.  
§   M. Ostendorf, Speech Technology and Information Access, IEEE Signal Processing Magazine 25(3), May 2008.  
§   L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, vol. 77, No. 2, February 1989  
§   A. V. Oppenheim and R. W. Schafer, "From Frequency to Quefrency: A History of the Cepstrum," IEEE Signal Processing Magazine 21(5), September 2004.  
§   A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1,  1977  
§   J. 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  
§   J. W. Picone, “Signal modeling techniques in speech recognition,” proceedings of the IEEE, September 1993, pp. 1215-1247  
§   R. Rosenfeld, ”Two Decades of Statistical Language Modeling: Where Do We Go from Here?,” Proceedings of IEEE, August, 2000  
§   H. Ney, “Progress in Dynamic Programming Search for LVCSR,” Proceedings of the IEEE, August 2000  
§   Aubert, X. L., "An Overview of Decoding Techniques for Large Vocabulary Continuous Speech Recognition," Computer Speech and Language, vol. 16, 2002, pp. 89-114.  
§   H. Hermansky, "Should Recognizers Have Ears?", Speech Communication, 25(1-3), Speech Communication, 25(1-3), 1998.  
§   J. R. Bellegarda, "Statistical Language Model Adaptation: Review and Perspectives," Speech Communication, vol. 42, no.1, pp. 93-108, 2004.  
§   B. Roark, "A survey of discriminative language modeling approaches for large vocabulary continuous speech recognition," in Large Margin and Kernel Approaches to Speech and Speaker Recognition, J. Keshet and S. Bengio (Eds.), Wiley, 2009.  
§   L. Rabiner, B.H. Juang, "Speech Recognition: Statistical Methods," Encyclopedia of Language & Linguistics, pp. 1-18, 2006.  
§    P. Nguyen, "TechWare: Speech recognition software and resources on the web," IEEE Signal Processing Magazine 25(3), May 2009.  
§   J. B. Allen, F. Li, "Speech Perception and Cochlear Signal Processing," IEEE Signal Processing Magazine 25(4), July 2009.  
§   A. Orlitsky, N. P. Santhanam, J. Zhang, "Always Good Turing: Asymptotically Optimal Probability Estimation," Science, 17 October 2003.  
  Proceedings of IEEE 88(8), August, 2000 (Special Issue on Spoken Language Processing)  
§   Frederick Jelinek, "The Dawn of Statistical ASR and MT," Computational Linguistics, Vol. 35, No. 4. (1 December 2009), pp. 483-494.  
§   L. Deng and X. Li, "Machine learning paradigms for speech recognition: An overview," IEEE Transactions on Audio, Speech, and Language Processing, 21(5), pp. 1060 - 1089, May, 2013.  
§   H. Li, B. Ma and K. A. Lee, "Spoken Language Recognition: From Fundamentals to Practice," Proceedings of the IEEE, February 2013.  
  IEEE Signal Processing Magazine 22(5), September 2005  (Special Issue on Speech Technology and Systems in Human-Machine Communication)  
  IEEE Signal Processing Magazine 25(3), May 2008  (Special Issue on Spoken Language Technology)  
  IEEE Signal Processing Magazine 29(6), December 2012  (Special Issue on Fundamental Technologies in Modern Speech Recognition)  

Reference Presentations:

§   J. Droppo, Noise Robust Automatic Speech Recognition, a comprehensive tutorial talk given at EUSIPCO 2008  
§   B. Chen, Latent Semantic Approaches for Information Retrieval and Language Modeling, a talk given at Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., 2008  
§   B. Chen, Recent Developments in Chinese Spoken Document Search and Distillation, a talk given at Google Taipei, 2009