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Speech Recognition Using Articulatory and Excitation Source Features

  • K. Sreenivasa Rao
  • Manjunath K E

Part of the SpringerBriefs in Electrical and Computer Engineering book series

Also part of the SpringerBriefs in Speech Technology book sub series (BRIEFSSPEECHTECH)

Table of contents

  1. Front Matter
    Pages i-xi
  2. K. Sreenivasa Rao, Manjunath K.E.
    Pages 1-6
  3. K. Sreenivasa Rao, Manjunath K.E.
    Pages 7-15
  4. K. Sreenivasa Rao, Manjunath K.E.
    Pages 17-46
  5. K. Sreenivasa Rao, Manjunath K.E.
    Pages 47-63
  6. K. Sreenivasa Rao, Manjunath K.E.
    Pages 81-84
  7. Back Matter
    Pages 85-92

About this book

Introduction

This book discusses the contribution of articulatory and excitation source information in discriminating sound units. The authors focus on excitation source component of speech -- and the dynamics of various articulators during speech production -- for enhancement of speech recognition (SR) performance. Speech recognition is analyzed for read, extempore, and conversation modes of speech. Five groups of articulatory features (AFs) are explored for speech recognition, in addition to conventional spectral features. Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features. The authors close by discussing various combinations of spectral, articulatory and source features, and the desired models to enhance the performance of SR systems.

Keywords

Speech Recognition Using Excitation Source Features Speech Recognition Using Articulatory Features Speech Recognition Using System and Source Features Phone Recognition Using Excitation Source Features Phone Recognition Using Articulatory Features Phone Recognition Using System and Source Features Read, Extempore and Conversation Modes of Speech RMFCC and MPDSS features for Speech Recognition Combination of Articulatory & Source Features for Speech Recog. Hybrid Model For Speech Recognition

Authors and affiliations

  • K. Sreenivasa Rao
    • 1
  • Manjunath K E
    • 2
  1. 1.School of Information TechnologyIndian Institute of Technology Kharagpu School of Information TechnologyKharagpurIndia
  2. 2.KarnatakaIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-49220-9
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-49219-3
  • Online ISBN 978-3-319-49220-9
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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