An HMM-Based Approach to Automatic Phrasing for Mandarin Text-to-Speech Synthesis

Jing Zhu, Jian-Hua Li

Abstract: Automatic phrasing is essential to Mandarin text-to-speech synthesis. We select word format as target linguistic feature and propose an HMM-based approach to this issue. Then we define four states of prosodic positions for each word when employing a discrete hidden Markov model. The approach achieves high accuracy of roughly 82%, which is very close to that from manual labeling. Our experimental results also demonstrate that this approach has advantages over those part-of-speech-based ones.