Language modeling with stochastic automata

It is well known that language models are effective for increasing accuracy of speech and handwriting recognizers, but large language models are often required to achieve low model perplexity (or entropy) and still have adequate language coverage. We study three efficient methods for stochastic language modeling in the context of the stochastic pattern recognition problem and give results of a comparative performance analysis. In addition we show that a method which combines two of these language modeling techniques yields even better performance than the best of the single techniques tested.