NTNU Speech Processing Course

Speech Processing

FALL 2017
9:10 ~12:10 am, Sundays
Instructor: Dr. Berlin Chen (陳柏琳)

Topic List and Schedule:

09/10 Course Overview & Introduction Readings: 1. F. Jelinek, The Speech Recognition Problem, Chapter 1 of the book "Statistical Methods for Speech Recognition."
               2. L. Rabiner. The Power of Speech. Science, Vol. 301, pp. 1494-1495, Sep. 2003.
                 3. S. Young. "Talking to Machines," Royal Academy of Engineering Ingenia, 54, pp. 40-46, 2013.
                  4. Frederick Jelinek, "The Dawn of Statistical ASR and MT," Computational Linguistics, Vol. 35, No. 4. (1 December 2009), pp. 483-494.
                  5. X. Huang, J. Baker, R. Reddy, "A Historical Perspective of Speech Recognition," ACM Communications, Vol. 57, No. 1, 2014.

09/10 　 Hidden Markov Models for Speech Recognition Readings: L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,”
                                   Proceedings of the IEEE, vol. 77, No. 2, February 1989

　　 Hidden Markov Models for Speech Recognition HW#1: Hidden Markov Models (Forward/Backward and Viterbi Algorithms)

　　 Maximum Likelihood Estimation HW#2: Hidden Markov Models (Model Estimation)

　　 Spoken Language Structure 　

　　 Acoustic Modeling 　

　　 Language Modeling 　

　　 Language Modeling 　

　　 Search Algorithm 　

　　 Speech Signal Analysis 　

　　 Robustness 　

　　 Some Representation Learning Approaches for Speech Recognition and its 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, M. Mohan Sondhi, Yiteng Huang (ed.), Springer Handbook of Speech Processing, Springer, 2007 　

§ 　 Tuomas Virtanen, Rita Singh, Bhiksha Raj (ed.), Techniques for Noise Robustness in Automatic Speech Recognition, John Wiley & Sons, 2013 　

§ 　 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 　

§ 　 Dong Yu and Li Deng, "Automatic Speech Recognition: A Deep Learning Approach," Springer, 2015 　

§ 　 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 　

§ 　 G. McLachlan, T. Krishnan, The EM Algorithm and Extensons, 2nd Edition, Wiley, 2008 　

Reference Papers:

§ 　 L. Rabiner. The Power of Speech. Science, Vol. 301, pp. 1494-1495, Sep. 2003. 　

§ 　 S. Young. "Talking to Machines," Royal Academy of Engineering Ingenia, 54, pp. 40-46, 2013. 　

§ 　 Y. LeCun, Y. Bengio and G. Hinton, "Deep learning," Nature, 521, pp. 436-444, 2015 　

§ 　 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. 　

§ 　 Hynek Hermansky, "Should Recognizers Have Ears?", Speech Communication, 25(1-3), 1998. 　

§ 　 Hynek Hermansky, "Speech recognition from spectral dynamics", Sadhana, 36(5), 2011. 　

§ 　 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. 　

§ 　 X. Huang, J. Baker, R. Reddy, "A Historical Perspective of Speech Recognition," ACM Communications, Vol. 57, No. 1, 2014. 　

§ 　 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) 　

○ 　 Proceedings of IEEE 101(5), May 2013 (Special Issue on Speech Information Processing: Theory and Applications) 　

Reference Presentations/Web Pages:

§ 　 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 　

§ 　 S. Chen, D. Beeferman, R. Rosenfeld, Evaluation metrics for language models, NIST 　

09/10	Course Overview & Introduction	Readings: 1. F. Jelinek, The Speech Recognition Problem, Chapter 1 of the book "Statistical Methods for Speech Recognition." 2. L. Rabiner. The Power of Speech. Science, Vol. 301, pp. 1494-1495, Sep. 2003. 3. S. Young. "Talking to Machines," Royal Academy of Engineering Ingenia, 54, pp. 40-46, 2013. 4. Frederick Jelinek, "The Dawn of Statistical ASR and MT," Computational Linguistics, Vol. 35, No. 4. (1 December 2009), pp. 483-494. 5. X. Huang, J. Baker, R. Reddy, "A Historical Perspective of Speech Recognition," ACM Communications, Vol. 57, No. 1, 2014.
09/10	Hidden Markov Models for Speech Recognition	Readings: L. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, vol. 77, No. 2, February 1989
	Hidden Markov Models for Speech Recognition	HW#1: Hidden Markov Models (Forward/Backward and Viterbi Algorithms)
	Maximum Likelihood Estimation	HW#2: Hidden Markov Models (Model Estimation)
	Spoken Language Structure
	Acoustic Modeling
	Language Modeling
	Language Modeling
	Search Algorithm
	Speech Signal Analysis
	Robustness
	Some Representation Learning Approaches for Speech Recognition and its Applications

§		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, M. Mohan Sondhi, Yiteng Huang (ed.), Springer Handbook of Speech Processing, Springer, 2007
§		Tuomas Virtanen, Rita Singh, Bhiksha Raj (ed.), Techniques for Noise Robustness in Automatic Speech Recognition, John Wiley & Sons, 2013
§		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
§		Dong Yu and Li Deng, "Automatic Speech Recognition: A Deep Learning Approach," Springer, 2015
§		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
§		G. McLachlan, T. Krishnan, The EM Algorithm and Extensons, 2nd Edition, Wiley, 2008

§		L. Rabiner. The Power of Speech. Science, Vol. 301, pp. 1494-1495, Sep. 2003.
§		S. Young. "Talking to Machines," Royal Academy of Engineering Ingenia, 54, pp. 40-46, 2013.
§		Y. LeCun, Y. Bengio and G. Hinton, "Deep learning," Nature, 521, pp. 436-444, 2015
§		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.
§		Hynek Hermansky, "Should Recognizers Have Ears?", Speech Communication, 25(1-3), 1998.
§		Hynek Hermansky, "Speech recognition from spectral dynamics", Sadhana, 36(5), 2011.
§		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.
§		X. Huang, J. Baker, R. Reddy, "A Historical Perspective of Speech Recognition," ACM Communications, Vol. 57, No. 1, 2014.
§		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)
○		Proceedings of IEEE 101(5), May 2013 (Special Issue on Speech Information Processing: Theory and Applications)

§		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
§		S. Chen, D. Beeferman, R. Rosenfeld, Evaluation metrics for language models, NIST