Word relevance modeling for speech recognition

Kuan Yu Chen, Hao Chin Chang, Berlin Chen, Hsin Min Wang

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

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, discovering either lexical co-occurrence or topic cues, have been developed to mitigate this problem with varying degrees of success. Among them, a more recent thread of work is the relevance modeling approach, which has shown promise to capture the lexical co-occurrence relationship between the entire search history and an upcoming word. However, a potential downside to such an approach is the need of resorting to a retrieval procedure to obtain relevance information; this is usually complex and time-consuming for practical applications. In this paper, we propose a word relevance modeling framework, which introduces 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 yet systematic way to render the lexical, topical, and proximity relationships between the search history and the upcoming word. Experiments on large vocabulary continuous speech recognition demonstrate the performance merits of the methods instantiated from this framework when compared to several existing methods.

Original languageEnglish
Title of host publication13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Pages998-1001
Number of pages4
Publication statusPublished - 2012 Dec 1
Event13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 - Portland, OR, United States
Duration: 2012 Sep 92012 Sep 13

Publication series

Name13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Volume2

Other

Other13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
CountryUnited States
CityPortland, OR
Period12/9/912/9/13

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Keywords

  • Adaptation
  • Language model
  • Lexical cooccurrence
  • Relevance
  • Topic cues

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

Cite this

Chen, K. Y., Chang, H. C., Chen, B., & Wang, H. M. (2012). Word relevance modeling for speech recognition. In 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 (pp. 998-1001). (13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012; Vol. 2).