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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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    Rights statement: © Yamagishi, J., Kobayashi, T., Renals, S., King, S., Zen, H., Toda, T., & Tokuda, K. (2007). Improved Average-Voice-based Speech Synthesis Using Gender-Mixed Modeling and a Parameter Generation Algorithm Considering GV. In SSW6-2007: 6th ISCA Workshop on Speech Synthesis. (pp. 125-130). International Speech Communication Association.

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http://www.isca-speech.org/archive_open/ssw6/ssw6_125.html
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

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.

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