A multi-path syllable to word decoder with language model optimization and automatic lexicon augmentation

Syllable to word decoding plays a very important role in Chinese large vocabulary continuous speech recognition (LVCSR). However, lack of word boundary and other characteristics of Chinese language prohibits the development of high quality language model decoder. In this paper, we present a multi-path search with language model optimization and automatic lexicon augmentation method to improve the accuracy of syllable to word decoding. The experiment result shows that our method achieves 34.76% character accuracy improvment over baseline performance