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 \u2014 thus enabling the use of extended distance dependencies \u2014in 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 \u2014 thus enabling the use of extended distance dependencies \u2014in an attempt to complement the locality of currently used trigram models. 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