Automatic phrase boundary labeling for Mandarin TTS corpus using context-dependent HMM

In this paper, an automatic prosodic phrase boundary labeling method for speech synthesis database is presented. This method can be divided into two stages: training stage and labeling stage. In training stage, context-dependent HMM, which is commonly adopted in the HMM-based parametric speech synthesis, is estimated using the training database with manual prosodic labeling. In labeling stage, the maximum likelihood criterion derived from the trained HMMs and the exhaustive search method are employed to find the optimal phrase boundary positions for an input sentence based on its acoustic features. The experimental results show that an F-score of 76.46% can be achieved for the prosodic phrase boundary detection of our Mandarin TTS corpus, which is close to the accuracy of experienced human labelers.