© 2015

Language Identification Using Excitation Source Features


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-xii
  2. K. Sreenivasa Rao, Dipanjan Nandi
    Pages 1-9
  3. K. Sreenivasa Rao, Dipanjan Nandi
    Pages 11-30
  4. K. Sreenivasa Rao, Dipanjan Nandi
    Pages 31-51
  5. K. Sreenivasa Rao, Dipanjan Nandi
    Pages 53-75
  6. K. Sreenivasa Rao, Dipanjan Nandi
    Pages 97-100
  7. Back Matter
    Pages 101-119

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.


Anguage Identification Combination of Spectral and Source Features Implicit and Explicit Source Features for LID Lang. Identification using Implicit Exci. Source Features Language Identification from Speech Language Identification using Excitation Source Features Language Identification using Source Features Language Recognition from Speech Magnitude and Phase Components of LP Parametric Excitation Source Features for Lang. Identification RMFCC and MPDSS Features for LID Residual for LID Sub-segmental, Segmental and Suprasegmental Source Features for for LID

Authors and affiliations

  1. 1.Indian Institute of Technology KharagpurWest BengalIndia
  2. 2.Indian Institute of Technology KharagpurWest BengalIndia

Bibliographic information

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