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Recognition Performance of a Structured Language Model

Ciprian Chelba·Frederick Jelinek
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Abstract

A new language model for speech recognition inspired by linguistic analysis is presented. The model develops hidden hierarchical structure incrementally and uses it to extract meaningful information from the word history - thus enabling the use of extended distance dependencies - in an attempt to complement the locality of currently used trigram models. The structured language model, its probabilistic parameterization and performance in a two-pass speech recognizer are presented. Experiments on the SWITCHBOARD corpus show an improvement in both perplexity and word error rate over conventional trigram models.

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Ciprian Chelba, Frederick Jelinek. 2000-01-24. Recognition Performance of a Structured Language Model. https://arxiv.org/abs/cs/0001022

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