A Bayesian approach to HMM-based speech synthesis

This paper proposes a new framework of speech synthesis based on the Bayesian approach. The Bayesian method is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters. In the proposed framework, all processes for constructing the system can be derived from one single predictive distribution which represents the basic problem of speech synthesis directly. Using HMM as the likelihood function and assuming some approximations, it can be regarded as an application of the variational Bayesian method to the HMM-based speech synthesis. Experimental results show that the proposed method outperforms the conventional one in a subjective test.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Kei Hashimoto, Heiga Zen, Yoshihiko Nankaku, Takashi Masuko, Keiichi Tokuda
• Keywords: Bayesian Method, ICASSP 2009, Predictive Distribution, Signal Processing, Speech Synthesis