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Language Identification Using Excitation Source Features

  • Book
  • © 2015

Overview

Authors:
  1. K. Sreenivasa Rao

    1. Indian Institute of Technology Kharagpur, West Bengal, India

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  2. Dipanjan Nandi

    1. Indian Institute of Technology Kharagpur, West Bengal, India

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    You can also search for this author in PubMed   Google Scholar

  • Discusses the excitation source component in the context of language identification, detailing how it can exploited for language discrimination in speech
  • Proposes robust signal processing methods for extracting the implicit excitation source features from LP residual signal
  • Presents explicit parametric features for representing the excitation source component of speech
  • 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-xii
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  2. Introduction

    • K. Sreenivasa Rao, Dipanjan Nandi
    Pages 1-9

  3. Language Identification—A Brief Review

    • K. Sreenivasa Rao, Dipanjan Nandi
    Pages 11-30

  4. Implicit Excitation Source Features for Language Identification

    • K. Sreenivasa Rao, Dipanjan Nandi
    Pages 31-51

  5. Parametric Excitation Source Features for Language Identification

    • K. Sreenivasa Rao, Dipanjan Nandi
    Pages 53-75

  6. Complementary and Robust Nature of Excitation Source Features for Language Identification

    • K. Sreenivasa Rao, Dipanjan Nandi
    Pages 77-96

  7. Summary and Conclusion

    • K. Sreenivasa Rao, Dipanjan Nandi
    Pages 97-100

  8. Back Matter

    Pages 101-119
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Keywords

About this book

This book discusses the contribution of excitation source information in discriminating language. The authors focus on the excitation source component of speech for enhancement of language identification (LID) performance. Language specific features are extracted using two different modes: (i) Implicit processing of linear prediction (LP) residual and (ii) Explicit parameterization of linear prediction residual. The book discusses how in implicit processing approach, excitation source features are derived from LP residual, Hilbert envelope (magnitude) of LP residual and Phase of LP residual; and in explicit parameterization approach, LP residual signal is processed in spectral domain to extract the relevant language specific features. The authors further extract source features from these modes, which are combined for enhancing the performance of LID systems. The proposed excitation source features are also investigated for LID in background noisy environments. Each chapter of this book provides the motivation for exploring the specific feature for LID task, and subsequently discuss the methods to extract those features and finally suggest appropriate models to capture the language specific knowledge from the proposed features. Finally, the book discuss about various combinations of spectral and source features, and the desired models to enhance the performance of LID systems.

Authors and Affiliations

  • Indian Institute of Technology Kharagpur, West Bengal, India

    K. Sreenivasa Rao, Dipanjan Nandi

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