Speech recognition systems incorporate a language model which, at each stage of the recognition task, assigns a probability of occurrence to each word in the vocabulary. A class of Markov language models identified by Jclinck has achieved considerable success in this domain. A modification of the Markov approach, which assigns higher probabilities to recently used words, is proposed and tested against a pure Markov model. Parameter calculation and comparison of the two models both involve use of the LOB Corpus of tagged modern English.

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.