Prosodic Boundary Prediction Based on Maximum Entropy Model with Error-Driven Modification

摘要

Prosodic boundary prediction is the key to improving the intelligibility and naturalness of synthetic speech for a TTS system. This paper investigated the problem of automatic segmentation of prosodic word and prosodic phrase, which are two fundamental layers in the hierarchical prosodic structure of Mandarin Chinese. Maximum Entropy (ME) Model was used at the front end for both prosodic word and prosodic phrase prediction, but with different feature selection schemes. A multi-pass prediction approach was adopted. Besides, an error-driven rule-based modification module was introduced into the back end to amend the initial prediction. Experiments showed that this combined approach outperformed many other methods like C4.5 and TBL.