THE APPLICABILITY OF LANGUAGE MODEL ADAPTATION FOR THE BROADCAST NEWS TASK
P.R. Clarkson and A.J. Robinson
Adaptive language models have consistently been shown to lead to a significant reduction in language model perplexity compared to the equivalent static trigram model on many data sets. When these language models have been applied to speech recognition, however, they have seldom resulted in a corresponding reduction in word error rate. This paper will investigate some of the possible reasons for this apparent discrepancy, and will explore the circumstances under which adaptive language models can be useful. We will concentrate on cache-based and mixture-based models and their use on the Broadcast News task.
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