IMPROVING THE SYLLABLE-SYNCHRONOUS NETWORK SEARCH ALGORITHM FOR WORD DECODING IN CONTINUOUS CHINESE SPEECH RECOGNITION

The previously proposed syllable-synchronous network search (SSNS) algorithm plays a very important role in the word decoding of the continuous Chinese speech recognition and achieves a satisfying performance. Several related key factors that may affect the overall word decoding effect are carefully studied in this paper, including the perfecting of the vocabulary, the big-discount Turing re-estimating of the N-Gram probabilities, and the managing of the searching path buffers. Based on these discussions, corresponding approaches to improve the SSNS algorithm are proposed. Compared to the previous version of SSNS algorithm, the new version decreases the Chinese character error rate (CCER) in the word decoding by 42.1 % across a database consisting of a large number of testing sentences (syllable strings)