Deriving phrase-based language models
Peter A. Heeman, Géraldine Damnati · 2002
Phrase-based language models have grown in popularity since they allow the speech recognition process to make use of more context in recognizing the words. Previous approaches have used perplexity reduction to identify groups of words to be linked into phrases and have used these phrases as the basis for computing the language model probabilities. In this paper, we argue that perplexity reduction is only one of three aspects to be considered in choosing the phrases. We also argue that the chosen phrases should not be the basis for computing the language model probabilities. Rather, the probabilities should be derived from a language model built at the lexical level.