A best-first language processing model integrating the unification grammar and Markov language model for speech recognition applications

摘要

A language processing model is proposed in which the grammatical approach of unification grammar and the statistical approach of Markov language models are properly integrated in a word lattice chart parsing algorithm with different best-first parsing strategies. This model has been successfully implemented in experiments on Mandarin speech recognition although it is language-independent. Test results show that significant improvements in both correct rate of recognition and computation speed can be achieved. A correct rate of 93.8% and 5 s per sentence on an IBM PC/AT, as compared with 73.8% and 25 s using unification grammar alone and 82.2% and 3 s using a Markov language model alone, was achieved. This high performance is due to the effective rejection of noisy word hypothesis interferences; that is, the unification-based grammatical analysis eliminates all illegal combinations, while the Markovian probabilities of constituents combined with the considerations on constituent length indicate the correct direction of processing.