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Source modeling techniques for quali...

Rao, K. Sreenivasa.

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Source modeling techniques for quality enhancement in statistical parametric speech synthesis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Source modeling techniques for quality enhancement in statistical parametric speech synthesis/ by K. Sreenivasa Rao, N. P. Narendra.
Author:	Rao, K. Sreenivasa.
other author:	Narendra, N. P.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xii, 136 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Background and literature review -- Chapter 3. Robust voicing detection and F0 estimation method -- Chapter 4. Parametric approach of modeling the source signal -- Chapter 5. Hybrid approach of modeling the source signal -- Chapter 6. Generation of creaky voice -- Chapter 7. Summary and conclusions.
Contained By:	Springer eBooks
Subject:	Speech synthesis. -
Online resource:	https://doi.org/10.1007/978-3-030-02759-9
ISBN:	9783030027599

Source modeling techniques for quality enhancement in statistical parametric speech synthesis
Rao, K. Sreenivasa.

Source modeling techniques for quality enhancement in statistical parametric speech synthesis[electronic resource] /by K. Sreenivasa Rao, N. P. Narendra. - Cham :Springer International Publishing :2019. - xii, 136 p. :ill., digital ;24 cm. - SpringerBriefs in speech technology, studies in speech signal processing, natural language understanding, and machine learning,2191-737X. - SpringerBriefs in speech technology, studies in speech signal processing, natural language understanding, and machine learning..

Chapter 1. Introduction -- Chapter 2. Background and literature review -- Chapter 3. Robust voicing detection and F0 estimation method -- Chapter 4. Parametric approach of modeling the source signal -- Chapter 5. Hybrid approach of modeling the source signal -- Chapter 6. Generation of creaky voice -- Chapter 7. Summary and conclusions.

This book presents a statistical parametric speech synthesis (SPSS) framework for developing a speech synthesis system where the desired speech is generated from the parameters of vocal tract and excitation source. Throughout the book, the authors discuss novel source modeling techniques to enhance the naturalness and overall intelligibility of the SPSS system. This book provides several important methods and models for generating the excitation source parameters for enhancing the overall quality of synthesized speech. The contents of the book are useful for both researchers and system developers. For researchers, the book is useful for knowing the current state-of-the-art excitation source models for SPSS and further refining the source models to incorporate the realistic semantics present in the text. For system developers, the book is useful to integrate the sophisticated excitation source models mentioned to the latest models of mobile/smart phones. Presents the efficient excitation source modeling techniques for generating high quality speech; Includes a combination of both waveform and parametric methods to enhance the quality of synthesis; Features and methods that need less memory and computational requirements than others, allowing them to be integrated to smart phones and smaller devices.

ISBN: 9783030027599

Standard No.: 10.1007/978-3-030-02759-9doiSubjects--Topical Terms:

887882
Speech synthesis.

LC Class. No.: TK7882.S65 / R365 2019

Dewey Class. No.: 006.54

Source modeling techniques for quality enhancement in statistical parametric speech synthesis
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100 1 $a Rao, K. Sreenivasa. $3 2058221
245 1 0 $a Source modeling techniques for quality enhancement in statistical parametric speech synthesis $h [electronic resource] / $c by K. Sreenivasa Rao, N. P. Narendra.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xii, 136 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in speech technology, studies in speech signal processing, natural language understanding, and machine learning, $x 2191-737X
505 0 $a Chapter 1. Introduction -- Chapter 2. Background and literature review -- Chapter 3. Robust voicing detection and F0 estimation method -- Chapter 4. Parametric approach of modeling the source signal -- Chapter 5. Hybrid approach of modeling the source signal -- Chapter 6. Generation of creaky voice -- Chapter 7. Summary and conclusions.
520 $a This book presents a statistical parametric speech synthesis (SPSS) framework for developing a speech synthesis system where the desired speech is generated from the parameters of vocal tract and excitation source. Throughout the book, the authors discuss novel source modeling techniques to enhance the naturalness and overall intelligibility of the SPSS system. This book provides several important methods and models for generating the excitation source parameters for enhancing the overall quality of synthesized speech. The contents of the book are useful for both researchers and system developers. For researchers, the book is useful for knowing the current state-of-the-art excitation source models for SPSS and further refining the source models to incorporate the realistic semantics present in the text. For system developers, the book is useful to integrate the sophisticated excitation source models mentioned to the latest models of mobile/smart phones. Presents the efficient excitation source modeling techniques for generating high quality speech; Includes a combination of both waveform and parametric methods to enhance the quality of synthesis; Features and methods that need less memory and computational requirements than others, allowing them to be integrated to smart phones and smaller devices.
650 0 $a Speech synthesis. $3 887882
650 1 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Natural Language Processing (NLP) $3 3381674
650 2 4 $a Computational Linguistics. $3 893900
700 1 $a Narendra, N. P. $3 3383123
710 2 $a SpringerLink (Online service) $3 836513
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830 0 $a SpringerBriefs in speech technology, studies in speech signal processing, natural language understanding, and machine learning. $3 3378748
856 4 0 $u https://doi.org/10.1007/978-3-030-02759-9
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