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Predicting Prosody from Text for Text-to-Speech SynthesisModeling Duration

Predicting Prosody from Text for Text-to-Speech Synthesis: Modeling Duration [This chapter presents modeling of durations of syllables. Features representing linguistic and production constraints are proposed for modeling the durations of syllables. Feedforward Neural Network (FFNN) and Support Vector Machines (SVMs) are proposed for predicting the durations of syllables. The prediction accuracy of the proposed models is compared with Classification and Regression Tree (CART) models. The effect of different constraints and their interactions in modeling the durations of syllables is examined. Finally, the proposed duration models have demonstrated for capturing the language-specific and speaker-specific information.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Predicting Prosody from Text for Text-to-Speech SynthesisModeling Duration

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/lp/springer-journals/predicting-prosody-from-text-for-text-to-speech-synthesis-modeling-RD0819ourF
Publisher
Springer New York
Copyright
© Springer Science+Business Media New York 2012
ISBN
978-1-4614-1337-0
Pages
41 –63
DOI
10.1007/978-1-4614-1338-7_4
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter presents modeling of durations of syllables. Features representing linguistic and production constraints are proposed for modeling the durations of syllables. Feedforward Neural Network (FFNN) and Support Vector Machines (SVMs) are proposed for predicting the durations of syllables. The prediction accuracy of the proposed models is compared with Classification and Regression Tree (CART) models. The effect of different constraints and their interactions in modeling the durations of syllables is examined. Finally, the proposed duration models have demonstrated for capturing the language-specific and speaker-specific information.]

Published: Mar 27, 2012

Keywords: Syllable Duration; CART Models; AANN Model; Duration Constraints; Syllable Position

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