Abstract

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. 1. INTRODUCTION The performance of an automatic speech recognition system can depend critically on the suitability of its language model. For example, a system trained to recognise speech read from the Wall Street Journal will be equipped with a language model trained on many millions of words from previous editions of the newspaper, and will perform very well on its specified task...

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