Recognition performance of a structured language model

نویسندگان

  • Ciprian Chelba
  • Frederick Jelinek
چکیده

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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عنوان ژورنال:
  • CoRR

دوره cs.CL/0001022  شماره 

صفحات  -

تاریخ انتشار 1999