Combining of confidence measures under Bayesian framework for speech recognition

Tae Yoon Kim, Hanseok Ko

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

Abstract

Bayesian combining of confidence measures is proposed for speech recognition. Centralized and distributed schemes are considered for confidence measure combining under Bayesian framework. Centralized combining is feature level fusion which combines the values of individual confidence scores and makes a final decision. In contrast, distributed combining is decision level fusion which combines the individual decision makings made by each individual confidence scoring method. Both methods are basically based on the statistical modeling of confidence features. In addition, adaptation of confidence score using the statistical models also presented. The proposed methods reduced the classification error rate by 17% from conventional single feature based confidence scoring method in isolated word Out-of-Vocabulary rejection test.

Original languageEnglish
Title of host publicationProceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2004
EditorsS.J. Ko
Pages164-168
Number of pages5
Publication statusPublished - 2004
EventProceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2004 - Seoul, Korea, Republic of
Duration: 2004 Nov 182004 Nov 19

Publication series

NameProceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2004

Other

OtherProceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2004
CountryKorea, Republic of
CitySeoul
Period04/11/1804/11/19

ASJC Scopus subject areas

  • Engineering(all)

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