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

Junichi Yamagishi, Takao Kobayashi, Stephen Renals, Simon King, Heiga Zen, Tomoki Toda, Keiichi Tokuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationSSW6-2007
Subtitle of host publication6th ISCA Workshop on Speech Synthesis
PublisherInternational Speech Communication Association
Pages125-130
Number of pages6
Publication statusPublished - 1 Aug 2007

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