Article details

Research area
Text to speech

The Seventh ISCA Tutorial and Research Workshop on Speech Synthesis, Kyoto, Japan



Refined statistical model tuning for 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 forward backward training. The results show that both the tune and refinement techniques lead to increased flexibility for speech synthesis modeling.

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