SpringerBriefs in Speech Technology

Predicting Prosody from Text for Text-to-Speech Synthesis

Authors: Rao, K. Sreenivasa

  • 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
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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.

Table of contents (8 chapters)

Buy this book

eBook $34.99
price for USA (gross)
  • ISBN 978-1-4614-1338-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $49.95
price for USA
  • ISBN 978-1-4614-1337-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Predicting Prosody from Text for Text-to-Speech Synthesis
Authors
Series Title
SpringerBriefs in Speech Technology
Copyright
2012
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4614-1338-7
DOI
10.1007/978-1-4614-1338-7
Softcover ISBN
978-1-4614-1337-0
Series ISSN
2191-737X
Edition Number
1
Number of Pages
XII, 130
Number of Illustrations and Tables
42 b/w illustrations
Topics