Dynamic language model adaptation using keyword category classification

This paper describes a language model adaptation method for improving speech recognition of keywords in spoken queries occurring in information retrieval tasks. The method dynamically adapts language models to keyword categories within a single utterance; it first estimates keyword categories and their positions in an input query utterance and then dynamically changes the weights for language models designed for individual keyword categories on the basis of the estimation results. The method has been evaluated in speech recognition experiments on television program retrieval tasks and has demonstrated a 22.0% reduction in keyword error rates.