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© 2011

Robust Speech Recognition of Uncertain or Missing Data

Theory and Applications

  • Dorothea Kolossa
  • Reinhold Häb-Umbach
Book

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Reinhold Haeb-Umbach, Dorothea Kolossa
    Pages 1-5
  3. Theoretical Foundations

    1. Front Matter
      Pages 7-7
    2. Ramón Fernandez Astudillo, Dorothea Kolossa
      Pages 35-64
  4. Applications: Noise Robustness

  5. Applications: Reverberation Robustness

    1. Front Matter
      Pages 223-223
    2. Marc Delcroix, Shinji Watanabe, Tomohiro Nakatani
      Pages 225-255
  6. Applications: Multiple Speakers and Modalities

    1. Front Matter
      Pages 291-291
    2. Marco Kühne, Roberto Togneri, Sven Nordholm
      Pages 293-318
    3. Eugen Hoffmann, Dorothea Kolossa, Reinhold Orglmeister
      Pages 319-344
    4. Alexander Vorwerk, Steffen Zeiler, Dorothea Kolossa, Ramón Fernandez Astudillo, Dennis Lerch
      Pages 345-375
  7. Back Matter
    Pages 377-380

About this book

Introduction

Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition.

The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.

 

Keywords

Audiovisual speech recognition Deconvolution Missing feature theory Noise robustness Packet loss Source separation Speech processing Speech recognition Uncertainty decoding

Editors and affiliations

  • Dorothea Kolossa
    • 1
  • Reinhold Häb-Umbach
    • 2
  1. 1.Institute of Communication AcousticsRuhr-Universität BochumBochumGermany
  2. 2., Dept. of Communications EngineeringUniversity of PaderbornPaderbornGermany

About the editors

Prof. Dr.-Ing. Dorothea Kolossa is a professor at the Institut für Kommunikationsakustik of the Ruhr-Universität Bochum, Germany; her research interests are automatic speech recognition, digital speech signal processing, and blind source separation.

Prof. Dr.-Ing. Reinhold Haeb-Umbach heads the Dept. of Communications Engineering of the University of Paderborn, Germany; his research interest are speech signal processing and automatic speech recognition, statistical learning and pattern recognition, and signal processing for digital communications.

 

Bibliographic information

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