This paper describes a number of approaches to refine and tune statistical models for speech synthesis. The first approach is to tune the sizes of the decision trees for central phonemes in a context. The second approach is a refinement technique for HMM models; a variable number of states for hidden semi- Markov models is emulated. A so-called “hard state-skip” training technique is introduced into the standard forwardbackward training. The results show that both the tune and refinement techniques lead to increased flexibility for speech synthesis modeling.
Index Terms: TTS, HSMM, decision tree, hard skip-state
Cite as: Shao, X., Pollet, V., Breen, A. (2010) Refined statistical model tuning for speech synthesis. Proc. 7th ISCA Workshop on Speech Synthesis (SSW 7), 284-287
@inproceedings{shao10_ssw,
author={Xu Shao and Vincent Pollet and Andrew Breen},
title={{Refined statistical model tuning for speech synthesis}},
year=2010,
booktitle={Proc. 7th ISCA Workshop on Speech Synthesis (SSW 7)},
pages={284--287}
}