Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods / Edition 1

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Overview

"This book discusses large margin and kernel methods for speech and speaker recognition." Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book.
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Product Details

  • ISBN-13: 9780470696835
  • Publisher: Wiley
  • Publication date: 2/24/2009
  • Edition number: 1
  • Pages: 268
  • Product dimensions: 6.80 (w) x 9.80 (h) x 0.80 (d)

Meet the Author

Dr Joseph Keshet, IDIAP, Switzerland

Dr Keshet  received his B.Sc. and M.Sc. in electrical engineering from the Tel-Aviv University, Tel-Aviv, Israel, in 1994 and 2002, respectively. He got his Ph.D. from the Hebrew University of Jerusalem, Israel in 2007. From 1994 to 2002, he was with the Israeli Defense Forces (Intelligence Corps), where he was in charge of advanced research activities in the fields of speech coding. Since 2007, he is a research scientist in speech recognition at IDIAP Research Institute, Martigny, Switzerland.

Dr Samy Bengio, Google, California, US

Dr Bengio received his M.Sc. and Ph.D. degrees in Computer Science from University of Montreal in 1989 and 1993 respectively. Between 1999 and 2006, he was a senior researcher in statistical machine learning at IDIAP Research Institute, where he supervised PhD students and postdoctoral fellows working on many areas of machine learning. He is the author/co-author of more than 160 international publications, including 30 journal papers. He has organized several international workshops (such as the MLMI series) and been in the organization committee of several well known conferences (such as NIPS). Since early 2007, he is a research scientist in machine learning at Google, in Mountain View, California.

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Table of Contents

I Foundations 1

1 Introduction Samy Bengio Bengio, Samy Joseph Keshet Keshet, Joseph 3

2 Theory and Practice of Support Vector Machines Optimization Shai Shalev-Shwartz Shalev-Shwartz, Shai Nathan Srebo Srebo, Nathan 11

3 From Binary Classification to Categorial Prediction Koby Crammer Crammer, Koby 27

II Acoustic Modeling 51

4 A Large Margin Algorithm for Forced Alignment Joseph Keshet Keshet, Joseph Shai Shalev-Shwartz Shalev-Shwartz, Shai Yomm Singer Singer, Yomm Dan Chazan Chazan, Dan 53

5 A Kernel Wrapper for Phoneme Sequence Recognition Joseph Keshet Keshet, Joseph Dan Chazan Chazan, Dan 69

6 Augmented Statistical Models: Using Dynamic Kernels for Acoustic Models Mark J. F. Gales Gales, Mark J. F. 83

7 Large Margin Training of Continuous Density Hidden Markov Models Fei Sha Fei, Sha Lawrence K. Saul Saul, Lawrence K. 101

III Language Modeling 115

8 A Survey of Discriminative Language Modeling Approaches for Large Vocabulary Continuous Speech Recognition Brian Roark Roark, Brian 117

9 Large Margin Methods for Part-of-Speech Tagging Yasemin Altun Altun, Yasemin 139

10 A Proposal for a Kernel Based Algorithm for Large Vocabulary Continuous Speech Recognition Joseph Keshet Keshet, Joseph 159

IV Applications 173

11 Discriminative Keyword Spotting David Grangier Grangier, David Joseph Keshet Keshet, Joseph Samy Bengio Bengio, Samy 175

12 Kernel-based Text-independent Speaker Verification Johnny Mariethoz Mariethoz, Johnny Samy Bengio Bengio, Samy Yves Grandvalet Grandvalet, Yves 195

13 Spectral Clustering for Speech Separation Francis R. Bach Bach, Francis R. Michael I. Jordan Jordan, Michael I. 221

Index 251

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