Title: Incremental Language Models for Speech Recognition Using Finite-State Transducers

Authors: Hans J.G.A. Dolfing, I. Lee Hetherington

Abstract: In the context of the weighted finite-state transducer approach to speech recognition, we investigate a novel decoding strategy to deal with very large n-gram language models often used in large-vocabulary systems. In particular, we present an alternative to full, static expansion and optimization of the finite-state transducer network. This alternative is useful when the individual knowledge sources, modeled as transducers, are too large to be composed and optimized. While the recognition decoder perceives a single, weighted finite-state transducer, we apply a divide-and-conquer technique to split the language model into two parts which add up exactly to the original language model. We investigate the merits of these `incremental language models' and present some initial results.

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