Integrating Articulatory Features into HMM-based Parametric Speech Synthesis

Abstract

This paper presents an investigation of ways to integrate articulatory features into Hidden Markov Model (HMM)-based parametric speech synthesis, primarily with the aim of improving the performance of acoustic parameter generation. The joint distribution of acoustic and articulatory features is estimated during training and is then used for parameter generation at synthesis time in conjunction with a maximum-likelihood criterion. Different model structures are explored to allow the articulatory features to influence acoustic modeling: model clustering, state synchrony and cross-stream feature dependency. The results of objective evaluation show that the accuracy of acoustic parameter prediction can be improved when shared clustering and asynchronous-state model structures are adopted for combined acoustic and articulatory features. More significantly, our experiments demonstrate that modeling the dependency between these two feature streams can make speech synthesis more flexible. The characteristics of synthetic speech can be easily controlled by modifying generated articulatory features as part of the process of acoustic parameter generation.