Paper: An HMM-Based Approach To Automatic Phrasing For Mandarin Text-To-Speech Synthesis

ACL ID P06-2125
Title An HMM-Based Approach To Automatic Phrasing For Mandarin Text-To-Speech Synthesis
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

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.