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Automatic Speech and Speaker Recognition

Advanced Topics

  • Chin-Hui Lee
  • Frank K. Soong
  • Kuldip K. Paliwal

Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 355)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. L. R. Rabiner, B.-H. Juang, C.-H. Lee
    Pages 1-30
  3. Chin-Hui Lee, Jean-Luc Gauvain
    Pages 83-107
  4. B.-H. Juang, W. Chou, C.-H. Lee
    Pages 109-132
  5. A. Higgins, L. Bahler, J. Porter
    Pages 211-232
  6. Tony Robinson, Mike Hochberg, Steve Renals
    Pages 233-258
  7. Hervé Bourlard, Nelson Morgan
    Pages 259-283
  8. Michael D. Riley, Andrej Ljolje
    Pages 285-301
  9. Richard C. Rose
    Pages 303-329
  10. Brian A. Hanson, Ted H. Applebaum, Jean-Claude Junqua
    Pages 331-356
  11. Richard M. Stern, Alejandro Acero, Fu-Hua Liu, Yoshiaki Ohshima
    Pages 357-384
  12. P. S. Gopalakrishnan, L. R. Bahl
    Pages 413-428
  13. Richard Schwartz, Long Nguyen, John Makhoul
    Pages 429-456
  14. X. Huang, A. Acero, F. Alleva, M. Hwang, L. Jiang, M. Mahajan
    Pages 481-508
  15. Back Matter
    Pages 509-517

About this book

Introduction

Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance.
Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization.
Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.

Keywords

Hardware Potential Signal algorithms information modeling network networks pattern recognition programming signal processing speech recognition

Editors and affiliations

  • Chin-Hui Lee
    • 1
  • Frank K. Soong
    • 1
  • Kuldip K. Paliwal
    • 2
  1. 1.AT&T Bell LaboratoriesMurray HillUSA
  2. 2.School of Microelectronic EngineeringGriffith UniversityAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-1367-0
  • Copyright Information Springer-Verlag US 1996
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8590-8
  • Online ISBN 978-1-4613-1367-0
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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