Dynamic tuning of language model score in speech recognition using a confidence measure

Sherif Abdou, Michael S Scordilis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Speech recognition errors limit the capability of language models to predict subsequent words correctly. An effective way to enhance the functions of the language model is by using confidence measures. Most of current efforts for developing confidence measures for speech recognition focus on applying these measures to the final recognition result. However, using these measures early in the search process may guide the search to more promising paths. In this work we propose to use a word-based acoustic confidence metric estimated from word posterior probability to dynamically tune the contribution of the language model score. The performance of this approach was tested on a conversational telephone speech corpus and results show significant reductions in recognition error rates.

Original languageEnglish (US)
Title of host publication7th International Conference on Spoken Language Processing, ICSLP 2002
PublisherInternational Speech Communication Association
Pages397-400
Number of pages4
StatePublished - 2002
Event7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States
Duration: Sep 16 2002Sep 20 2002

Other

Other7th International Conference on Spoken Language Processing, ICSLP 2002
CountryUnited States
CityDenver
Period9/16/029/20/02

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Abdou, S., & Scordilis, M. S. (2002). Dynamic tuning of language model score in speech recognition using a confidence measure. In 7th International Conference on Spoken Language Processing, ICSLP 2002 (pp. 397-400). International Speech Communication Association.