Semi Supervised Learning For Prediction Of Prosodic Phrase Boundaries In Chinese Tts Using Conditional Random Fields

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 from a limited amount of labeled training examples and some amount of unlabeled data using conditional random fields. Some useful unlabeled data are chosen based on the assigned labels and the prediction probabilities of the current learned model. The useful unlabeled data is then exploited to improve the learning. Experiments show that the approach improves overall performance. The precision and recall ratio are improved.