Class-based language model adaptation using mixtures of word-class weights
Gareth Moore, Steve J. Young · 2000
This paper describes the use of a weighted mixture of classbased 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. 1 Introduction Previously, weighted mixtures of word n-gram language models have been used to provide a topic adaptation method for large vocabulary speech recognition [3]. A disadvantage of this method, however, is that these models require large numbers of parameters per topic, which in turn necessitates a large quantity of training data for each topic and associated storage space per topic in the resulting language model. We investigate an alternative approach to topic mixtures within the n-gram paradigm by using a class-based n-...