Bayesian fusion of confidence measures for speech recognition

Tae Yoon Kim, Hanseok Ko

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

The application of Bayesian fusion of confidence measures to speech recognition is proposed. Feature level, decision level, and hybrid fusion are considered under the Bayesian framework. The use of speaker-adapted feature-level Bayesian fusion reduced the error rate by 19.4% as compared to the conventional single feature-based confidence scoring in an isolated word out-of-vocabulary rejection test. The decision-level Bayesian fusion also showed better performance than the majority rule. Finally, hybrid Bayesian fusion, which can combine both confidence measure features and local decisions, achieved the best performance.

Original languageEnglish
Pages (from-to)871-874
Number of pages4
JournalIEEE Signal Processing Letters
Volume12
Issue number12
DOIs
Publication statusPublished - 2005 Dec 1

Fingerprint

Confidence Measure
Speech Recognition
Speech recognition
Fusion
Fusion reactions
Majority Rule
Rejection
Scoring
Confidence
Error Rate

Keywords

  • Adaptive confidence scoring
  • Bayesian fusion
  • Confidence measure (CM)
  • Speech recognition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

Bayesian fusion of confidence measures for speech recognition. / Kim, Tae Yoon; Ko, Hanseok.

In: IEEE Signal Processing Letters, Vol. 12, No. 12, 01.12.2005, p. 871-874.

Research output: Contribution to journalArticle

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