The Seventh ISCA Tutorial and Research Workshop on Speech Synthesis
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
Bibliographic reference. Shao, Xu / Pollet, Vincent / Breen, Andrew (2010): "Refined statistical model tuning for speech synthesis", In SSW7-2010, 284-287.