Article details

Research area
Text to speech

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

Date
2010

Author(s)

Refined statistical model tuning for speech synthesis

Synopsis:

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.

Read/download now