Prosodic Words Prediction from Lexicon Words with CRF and TBL Joint Method

Abstract

Predicting prosodic words boundaries will directly influence the naturalness of synthetic speech, because prosodic word is at the lowest level of prosody hierarchy. In this paper, a Chinese prosodic phrasing method based on CRF and TBL model is proposed. First a CRF model is trained to predict the prosodic words boundaries from lexicon words. After that we apply a TBL based error driven learning approach to refine the results. The experiments shows that this joint method performs much better than HMM.