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Neural text-to-speech synthesis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural text-to-speech synthesisby Xu Tan.
Author:	Tan, Xu.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	1 online resource (xxv, 201 p.) :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science)
Online resource:	https://doi.org/10.1007/978-981-99-0827-1
ISBN:	9789819908271$q(electronic bk.)

Neural text-to-speech synthesis
Tan, Xu.

Neural text-to-speech synthesis[electronic resource] /by Xu Tan. - Singapore :Springer Nature Singapore :2023. - 1 online resource (xxv, 201 p.) :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-306X. - Artificial intelligence: foundations, theory, and algorithms..

Chapter 1. Introduction -- Part 1. Preliminary -- Chapter 2. Basics of Spoken Language Processing -- Chapter 3. Basics of Deep Learning -- Part 2. Key Components in TTS -- Chapter 4. Text Analyses -- Chapter 5. Acoustic Models -- Chapter 6. Vocoders -- Chapter 7. Fully End-to-End TTS -- Part 3. Advanced Topics in TTS -- Chapter 8. Expressive and Controllable TTS -- Chapter 9. Robust TTS -- Chapter 10. Model-Efficient TTS -- Chapter 11. Data-Efficient TTS -- Chapter 12. Beyond Text-to-Speech Synthesis -- Part 4. Summary and Outlook -- Chapter 13. Summary and Outlook.

Text-to-speech (TTS) aims to synthesize intelligible and natural speech based on the given text. It is a hot topic in language, speech, and machine learning research and has broad applications in industry. This book introduces neural network-based TTS in the era of deep learning, aiming to provide a good understanding of neural TTS, current research and applications, and the future research trend. This book first introduces the history of TTS technologies and overviews neural TTS, and provides preliminary knowledge on language and speech processing, neural networks and deep learning, and deep generative models. It then introduces neural TTS from the perspective of key components (text analyses, acoustic models, vocoders, and end-to-end models) and advanced topics (expressive and controllable, robust, model-efficient, and data-efficient TTS) It also points some future research directions and collects some resources related to TTS. This book is the first to introduce neural TTS in a comprehensive and easy-to-understand way and can serve both academic researchers and industry practitioners working on TTS.

ISBN: 9789819908271$q(electronic bk.)

Standard No.: 10.1007/978-981-99-0827-1doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Neural text-to-speech synthesis
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505 0 $a Chapter 1. Introduction -- Part 1. Preliminary -- Chapter 2. Basics of Spoken Language Processing -- Chapter 3. Basics of Deep Learning -- Part 2. Key Components in TTS -- Chapter 4. Text Analyses -- Chapter 5. Acoustic Models -- Chapter 6. Vocoders -- Chapter 7. Fully End-to-End TTS -- Part 3. Advanced Topics in TTS -- Chapter 8. Expressive and Controllable TTS -- Chapter 9. Robust TTS -- Chapter 10. Model-Efficient TTS -- Chapter 11. Data-Efficient TTS -- Chapter 12. Beyond Text-to-Speech Synthesis -- Part 4. Summary and Outlook -- Chapter 13. Summary and Outlook.
520 $a Text-to-speech (TTS) aims to synthesize intelligible and natural speech based on the given text. It is a hot topic in language, speech, and machine learning research and has broad applications in industry. This book introduces neural network-based TTS in the era of deep learning, aiming to provide a good understanding of neural TTS, current research and applications, and the future research trend. This book first introduces the history of TTS technologies and overviews neural TTS, and provides preliminary knowledge on language and speech processing, neural networks and deep learning, and deep generative models. It then introduces neural TTS from the perspective of key components (text analyses, acoustic models, vocoders, and end-to-end models) and advanced topics (expressive and controllable, robust, model-efficient, and data-efficient TTS) It also points some future research directions and collects some resources related to TTS. This book is the first to introduce neural TTS in a comprehensive and easy-to-understand way and can serve both academic researchers and industry practitioners working on TTS.
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