Prediction of Prosodic Phrase Boundaries in Chinese TTS Based on Conditional Random Fields and Transformation Based Learning

Hierarchical prosody structure generation is a key component for a speech synthesis system. One major feature of the prosody of Mandarin Chinese speech flow is prosodic phrase grouping. In this paper we proposed an approach for prediction of Chinese prosodic phrase boundaries in unrestricted Chinese text, which combines Conditional Random Fields (CRFs) model and TBL model. First a CRFs model is trained to predict the prosodic phrase boundaries. After that we apply a TBL based error driven learning approach to amend the initial prediction. A comparison is conducted between the new model and HMM for prosodic phrase break prediction. Experiments show that the combined approach improves overall performance. The precision and recall ratio are improved.