ENRICHING MANDARIN SPEECH RECOGNITION BY INCORPORATING A HIERARCHICAL PROSODY MODEL

Abstract

This paper presents a new probabilistic framework of Mandarin speech recognition by incorporating a sophisticated hierarchical prosody model into the conventional HMM-based system. The prosody model describes the relations of linguistic cues of various levels, break types and prosodic states which represent the prosody hierarchical structure, and prosody-related acoustic features. Aside from producing the recognized word sequences, the system also decodes other information including word's part-of-speech, punctuation marks, inter-syllable break types, and prosodic states of syllables. Experimental results on the TCC300 corpus, which consists of paragraphic utterances, showed that the proposed system significantly outperformed the baseline system. The word and character error rates decreased from 24.4% and 18.1% to 20.7% and 14.4% (or 15.2% and 20.4% relative improvements), respectively.