Language model adaptation using mixtures and an exponentially decaying cache

This paper presents two techniques for language model adaptation. The first is based on the use of mixtures of language models: the training text is partitioned according to topic, a language model is constructed for each component, and at recognition time appropriate weightings are assigned to each component to model the observed style of language. The second technique is based on augmenting the standard trigram model with a cache component in which words recurrence probabilities decay exponentially over time. Both techniques yield a significant reduction in perplexity over the baseline trigram language model when faced with multi-domain test text, the mixture-based model giving a 24% reduction and the cache-based model giving a 14% reduction. The two techniques attack the problem of adaptation at different scales, and as a result can be used in parallel to give a total perplexity reduction of 30%.