Improved Average-Voice-based Speech Synthesis Using Gender-Mixed Modeling and a Parameter Generation Algorithm Considering GV
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Date
08/2007Author
Yamagishi, Junichi
Kobayashi, Takao
Renals, Steve
King, Simon
Zen, Heiga
Toda, Tomoki
Tokuda, Keiichi
Metadata
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.