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Improved Average-Voice-based Speech Synthesis Using Gender-Mixed Modeling and a Parameter Generation Algorithm Considering GV

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ssw6-yamagishi.pdf (281.8Kb)
Date
08/2007
Author
Yamagishi, Junichi
Kobayashi, Takao
Renals, Steve
King, Simon
Zen, Heiga
Toda, Tomoki
Tokuda, Keiichi
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Abstract
For constructing a speech synthesis system which can achieve diverse voices, we have been developing a speaker independent approach of HMM-based speech synthesis in which statistical average voice models are adapted to a target speaker using a small amount of speech data. In this paper, we incorporate a high-quality speech vocoding method STRAIGHT and a parameter generation algorithm with global variance into the system for improving quality of synthetic speech. Furthermore, we introduce a feature-space speaker adaptive training algorithm and a gender mixed modeling technique for conducting further normalization of the average voice model. We build an English text-to-speech system using these techniques and show the performance of the system.
URI
http://hdl.handle.net/1842/2132
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  • CSTR publications
  • Linguistics and English Language publications

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