Structural maximum a posteriori speaker adaptation of speaking rate-dependent hierarchical prosodic model for Mandarin TTS

Abstract

In this paper, a structural maximum a posterior speaker adaptation method to adjust the existing speaking rate (SR) dependent hierarchical prosodic model (SR-HPM) to a new speaker's data for realizing a new voice of any given SR is discussed. The adaptive SR-HPM is formulated based on MAP estimation with a reference SR-HPM serving as an informative prior. The prior information provided by the reference SR-HPM is hierarchically organized by decision trees. The results of objective and subjective evaluations showed that the proposed method not only performed slightly better than the maximum likelihood-based model in the observed SR range of the target speaker's data, but also was much better in the unseen SR range.