A dynamic language model for speech recognition

Abstract

In the case of a trigram language model, the probability of the next word conditioned on the previous two words is estimated from a large corpus of text. The resulting static trigram language model (STLM) has fixed probabilities that are independent of the document being dictated. To improve the language model (LM), one can adapt the probabilities of the trigram language model to match the current document more closely. The partially dictated document provides significant clues about what words are more likely to be used next. Of many methods that can be used to adapt the LM, we describe in this paper a simple model based on the trigram frequencies estimated from the partially dictated document. We call this model a cache trigram language model (CTLM) since we are caching the recent history of words. We have found that the CTLM reduces the perplexity of a dictated document by 23%. The error rate of a 20,000-word isolated word recognizer decreases by about 5% at the beginning of a document and by about 24% after a few hundred words.