Relevance language modeling for speech recognition

Kuan Yu Chen, Berlin Chen

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

9 Citations (Scopus)

Abstract

Language models for speech recognition tend to be brittle across domains, since their performance is vulnerable to changes in the genre or topic of the text on which they are trained. A number of adaptation methods, exploring either lexical co-occurrence or topic cues, have been developed to mitigate this problem with varying degrees of success. In this paper, we study a novel use of relevance information for dynamic language model adaptation in speech recognition. It not only inherits the merits of several existing techniques but also provides a flexible but systematic way to render the lexical and topical relationships between a search history and an upcoming word. Empirical results on large vocabulary continuous speech recognition show that the methods deduced from our framework represent promising alternatives to the other existing language model adaptation methods compared in this paper.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages5568-5571
Number of pages4
DOIs
Publication statusPublished - 2011 Aug 18
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 2011 May 222011 May 27

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period11/5/2211/5/27

Keywords

  • adaptation
  • language model
  • lexical co-occurrence
  • relevance
  • speech recognition
  • topic cues

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Chen, K. Y., & Chen, B. (2011). Relevance language modeling for speech recognition. In 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings (pp. 5568-5571). [5947621] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2011.5947621