Traditional Chinese Parser and Language Modeling for Mandadin ASR

Abstract

A new approach of traditional Chinese parser to improving the language modeling of Mandarin speech recognition is proposed in this paper. The parser first uses a preprocessing to correct some word segmentation inconsistencies of the text corpus. It then employs a CRF-based word segmentation method and a CRF-based POS tagger to resegment the texts so as to generate better word strings for training an n-gram language model (LM) for ASR. Experimental results on the TCC-300 corpus showed that a word error rate (WER) of 13.4% was achieved by the proposed method. It is about 45% improvement on the relative WER reduction as compared with the previous system.