Acoustical and Environmental Robustness in Automatic Speech Recognition

  • Alejandro Acero

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Alejandro Acero
    Pages 1-16
  3. Alejandro Acero
    Pages 17-38
  4. Alejandro Acero
    Pages 39-66
  5. Alejandro Acero
    Pages 67-80
  6. Alejandro Acero
    Pages 81-99
  7. Alejandro Acero
    Pages 101-119
  8. Alejandro Acero
    Pages 121-129
  9. Alejandro Acero
    Pages 131-136
  10. Alejandro Acero
    Pages 137-142
  11. Back Matter
    Pages 143-186

About this book

Introduction

The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone or acoustical environment from the one with which they were trained. The use of microphones other than a "close­ talking" headset also tends to severely degrade speech recognition -performance. Even in relatively quiet office environments, speech is degraded by additive noise from fans, slamming doors, and other conversations, as well as by the effects of unknown linear filtering arising reverberation from surface reflections in a room, or spectral shaping by microphones or the vocal tracts of individual speakers. Speech-recognition systems designed for long-distance telephone lines, or applications deployed in more adverse acoustical environments such as motor vehicles, factory floors, oroutdoors demand far greaterdegrees ofenvironmental robustness. There are several different ways of building acoustical robustness into speech recognition systems. Arrays of microphones can be used to develop a directionally-sensitive system that resists intelference from competing talkers and other noise sources that are spatially separated from the source of the desired speech signal.

Keywords

Signal development digital signal processor filtering filters signal processing signal processor speech recognition tables

Authors and affiliations

  • Alejandro Acero
    • 1
  1. 1.Carnegie Mellon UniversityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-3122-7
  • Copyright Information Kluwer Academic Publishers 1993
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6366-8
  • Online ISBN 978-1-4615-3122-7
  • Series Print ISSN 0893-3405
  • About this book