Language Identification Using Excitation Source Features

Name: Language Identification Using Excitation Source Features
Brand: Springer International Publishing AG
SKU: 9783319177243
Price: 19305 HUF
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Szerző: K. Sreenivasa Rao, Dipanjan Nandi

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer International Publishing AG

Elérhetőség: Beszállítói készleten

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This book discusses the contribution of excitation source information in discriminating language. Th...

Információk a könyvről

Szerző

K. Sreenivasa Rao, Dipanjan Nandi

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2015

oldal

119

EAN

9783319177243

ISBN

3319177249

Enbook ID

09249923

Kiadó

Springer International Publishing AG

Súly

2117

Méretek

155 x 235 x 7

Teljes leírás

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.

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Language Identification Using Excitation Source Features

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