Language Identification Using Excitation Source Features

Name: Language Identification Using Excitation Source Features
Brand: Springer International Publishing AG
SKU: 9783319177243
Price: 212.42 PLN
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Autor: K. Sreenivasa Rao, Dipanjan Nandi

Język:

Oprawa: Miękka

Wydawca: Springer International Publishing AG

Dostępność: Dostępna u dostawcy

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

Informacje o książce

Autor

K. Sreenivasa Rao, Dipanjan Nandi

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2015

strony

119

EAN

9783319177243

ISBN

3319177249

Enbook ID

09249923

Wydawca

Springer International Publishing AG

Waga

2117

Wymiary

155 x 235 x 7

Pełny opis

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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