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Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

  • Silke¬†Goronzy

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2560)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2560)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Pages 1-5
  3. Pages 7-13
  4. Pages 37-56
  5. Pages 57-78
  6. Pages 105-107
  7. Pages 109-112
  8. Pages 135-138
  9. Pages 139-144
  10. Back Matter
    Pages 125-126

About this book

Introduction

Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems.
In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.

Keywords

Automat Automatic Speech Recognition Confidence Measures Human-Machine Interfaces MLLR Natural Language Processing Non-Native Accents Phone Duration Pronunciation Modeling Speaker Adaptation Speech Processing algorithms cognition modeling speech recognition

Editors and affiliations

  • Silke¬†Goronzy
    • 1
  1. 1.SCLE, MMI LabSony International (Europe) GmbHStuttgartGermany

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-36290-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-00325-0
  • Online ISBN 978-3-540-36290-6
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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