CLASS-BASED LANGUAGE MODEL ADAPTATION USING MIXTURES OF WORD-CLASS WEIGHTS
G.L. Moore and S.J. Young
This paper describes the use of a weighted mixture of class-based n-gram language models to perform topic adaptation. By using a fixed class n-gram history and variable word-given-class probabilities we obtain large improvements in the performance of the class-based language model, giving it similar accuracy to a word n-gram model, and an associated small but statistically significant improvement when we interpolate with a word-based n-gram language model.
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