Authors:
- Explores the intuitive relation between prosody and linguistic and production constraints
- Proposes non-linear models such as neural networks and support vector machines for capturing the prosodic information from the linguistic and production constraints
- Demonstrates the use of predicted prosodic knowledge for speech, speaker and language
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Speech Technology (BRIEFSSPEECHTECH)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
About this book
Predicting Prosody from Text for Text-to-Speech Synthesis covers the specific aspects of prosody, mainly focusing on how to predict the prosodic information from linguistic text, and then how to exploit the predicted prosodic knowledge for various speech applications. Author K. Sreenivasa Rao discusses proposed methods along with state-of-the-art techniques for the acquisition and incorporation of prosodic knowledge for developing speech systems.
Positional, contextual and phonological features are proposed for representing the linguistic and production constraints of the sound units present in the text. This book is intended for graduate students and researchers working in the area of speech processing.
Authors and Affiliations
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, School of Information Technology, Indian Institute of Technology, Kharagpur, India
K. Sreenivasa Rao
Bibliographic Information
Book Title: Predicting Prosody from Text for Text-to-Speech Synthesis
Authors: K. Sreenivasa Rao
Series Title: SpringerBriefs in Speech Technology
DOI: https://doi.org/10.1007/978-1-4614-1338-7
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Science+Business Media New York 2012
Softcover ISBN: 978-1-4614-1337-0Published: 27 April 2012
eBook ISBN: 978-1-4614-1338-7Published: 27 April 2012
Series ISSN: 2191-737X
Series E-ISSN: 2191-7388
Edition Number: 1
Number of Pages: XII, 130
Number of Illustrations: 42 b/w illustrations
Topics: Signal, Image and Speech Processing, Computational Linguistics, Natural Language Processing (NLP)