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Speech Recognition Using Articulatory and Excitation Source Features

  • Book
  • © 2017

Overview

Authors:
  1. K. Sreenivasa Rao

    1. School of Information Technology, Indian Institute of Technology Kharagpu School of Information Technology, Kharagpur, India

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  2. Manjunath K E

    1. Karnataka, India

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  • Focuses on articulatory features and various groups present within the general AFs
  • Proposes robust signal processing methods for extracting the excitation source features from LP residual signal
  • Discusses various mapping functions for extracting the AFs from spectral features and appropriate non-linear models for realizing the accurate mapping functions for the shape of vocal tract to movements of articulators
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Speech Technology (BRIEFSSPEECHTECH)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction

    • K. Sreenivasa Rao, Manjunath K.E.
    Pages 1-6

  3. Literature Review

    • K. Sreenivasa Rao, Manjunath K.E.
    Pages 7-15

  4. Articulatory Features for Phone Recognition

    • K. Sreenivasa Rao, Manjunath K.E.
    Pages 17-46

  5. Excitation Source Features for Phone Recognition

    • K. Sreenivasa Rao, Manjunath K.E.
    Pages 47-63

  6. Articulatory and Excitation Source Features for Phone Recognition in Read, Extempore and Conversation Modes of Speech

    • K. Sreenivasa Rao, Manjunath K.E.
    Pages 65-79

  7. Summary and Conclusion

    • K. Sreenivasa Rao, Manjunath K.E.
    Pages 81-84

  8. Back Matter

    Pages 85-92
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Keywords

About this book

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.

Authors and Affiliations

  • School of Information Technology, Indian Institute of Technology Kharagpu School of Information Technology, Kharagpur, India

    K. Sreenivasa Rao

  • Karnataka, India

    Manjunath K E

About the authors

K. Sreenivasa Rao is an Associate Professor at IIT Kharagpur. He has published seven books with Springer. He published 55 Journal publications, 25 book chapters and 115 conference publications.

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