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© 2011

Statistical Pronunciation Modeling for Non-Native Speech Processing

Benefits

  • Presents a new methodology for speech recognition of non-native speakers

  • Shows a proven and verified approach

Book

Part of the Signals and Communication Technology book series (SCT)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 1-4
  3. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 5-17
  4. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 19-23
  5. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 25-30
  6. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 31-46
  7. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 47-70
  8. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 71-83
  9. Rainer E. Gruhn, Wolfgang Minker, Satoshi Nakamura
    Pages 85-88
  10. Back Matter
    Pages 89-114

About this book

Introduction

In this work, the authors present a fully statistical approach to model non--native speakers' pronunciation. Second-language speakers pronounce words in multiple different ways compared to the native speakers. Those deviations, may it be phoneme substitutions, deletions or insertions, can be modelled automatically with the new method presented here.
The methods is based on a discrete hidden Markov model as a word pronunciation model, initialized on a standard pronunciation dictionary. The implementation and functionality of the methodology has been proven and verified with a test set of non-native English in the regarding accent.
The book is written for researchers with a professional interest in phonetics and automatic speech and speaker recognition.

Authors and affiliations

  1. 1.Harman/Becker Automotive Systems GmbHUlmGermany
  2. 2.Fak. Ingenieurwissenschaften und, ElektrotechnikUniversität UlmUlmGermany
  3. 3.Dept. Acoustics & Speech ResearchNICT-ATRKeihanna Science City, KyotoJapan

Bibliographic information

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