Language model adaptation in speech recognition using document maps

Abstract

We present speech experiments that were carried out to evaluate a topically focusing language model in large vocabulary speech recognition. An ordered topical clustering is first computed as a self-organized mapping of a large document collection. Language models are then trained for each text cluster or for several neighboring clusters. The obtained organized collection of language models is efficiently utilized in continuous speech recognition to concentrate on the model that corresponds closest to the current topic of discussion. The speech recognition experiments are carried out on a novel Finnish speech database. A property of Finnish that is particularly challenging for speech recognition is the extremely fast vocabulary growth that makes many of the standard word-based language modeling methods impractical for large vocabulary tasks.