In the case of a trlgr~m 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 mode] (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 ~re 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 ~ cache trigram language model (CTLM) since we are c~chlng the recent history of words. We have found that the CTLM red,aces 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.
Jelinek et al. REF proposed a cache trigram language model by using trigram frequencies estimated from the recent history of words.
A Dynamic Language Model For Speech Recognition
Workshop On Speech And Natural Language
Computer Science