An automatic acquisition method of statistic finite-state automaton for sentences

Statistic language models obtained from a large number of training samples play an important role in speech recognition. In order to obtain higher recognition performance, we should introduce long distance correlations between words. However, traditional statistic language models such as word n-grams and ergodic HMMs are insufficient for expressing long distance correlations between words. We propose an acquisition method for a language model based on HMnet taking into consideration long distance correlations and word location.