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. 1. INTRODUCTION The main goal of the present work is to develop and evaluate a language model that uses syntactic structure to model longdistance dependencies. The model we present is closely related to the one investigated in [1], however different in a few important aspects: ffl our model operates in a left-to-right manner, allowing the decoding of word lattices, as oppose...

Citations