Prosody Phrase Break Prediction Based on Maximum Entropy Model
Ren-Hua Wang · Zhongwen xinxi xuebao · 2004
In TTS (Text-To-Speech) systems, prosody phrase breaks can not be predicted with high accuracy, which slows down the improvement of naturalness of synthesized speech. In this paper, a maximum entropy based model for prosody phrase break prediction is proposed, and a comparison is conducted on large corpora between the new model and the decision tree based model which is the mainstream method for prosody phrase break prediction. The contribution of lexical feature set and influences of different cutoff values are also investigated. It is demonstrated that, utilizing the same feature set, maximum entropy based model makes an improvement of 5.5% on F-Score over decision tree based model. Integrating lexical information, an improvement of 9.4% over decision tree based model is achieved. In the end, it is pointed out that a maximum entropy model can be considered as a weighted rule system, which solves the problem of rule conflicting in an elegant way.