Improved Average-Voice-based Speech Synthesis Using Gender-Mixed Modeling and a Parameter Generation Algorithm Considering GV
Junichi Yamagishi, Takao Kobayashi, Steve J. Renals, Simon King, Heiga Zen, Tomoki Toda, Keiichi Tokuda · Institutional Repositories DataBase (IRDB) · 2007
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.