Bayesian fusion of confidence measures for speech recognition

Tae Yoon Kim, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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
Issue number12
Publication statusPublished - 2005 Dec


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

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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