Fast and accurate recognition of very-large-vocabulary continuous Mandarin speech for Chinese language with improved segmental probability modeling

This paper presents a fast and accurate recognition of continuous Mandarin speech with very large vocabulary using an improved segmental probability model (SPM) approach. In order to extensively utilize the acoustic and linguistic knowledge to further improve the recognition performance, a few special techniques are thus developed. Preliminary simulation results show that the final achievable rate for the base syllable recognition with the improved segmental probability modeling is as high as 91.62%, which indicates a 18.48% error rate reduction and more than 3 times faster than the well-studied sub-syllable-based CHMM. Also, a tone recognizer and a word-based Chinese language model are included and the achieved recognition accuracy for the final decoded Chinese characters is 92.10%.