Bayesian confidence scoring and adaptation techniques for speech recognition

Tae Yoon Kim, Hanseok Ko

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Bayesian combining of confidence measures is proposed for speech recognition. Bayesian combining is achieved by the estimation of joint pdf of confidence feature vector in correct and incorrect hypothesis classes. In addition, the adaptation of a confidence score using the pdf is presented. The proposed methods reduced the classification error rate by 18% from the conventional single feature based confidence scoring method in isolated word Out-of-Vocabulary rejection test.

Original languageEnglish
Pages (from-to)1756-1759
Number of pages4
JournalIEICE Transactions on Communications
VolumeE88-B
Issue number4
DOIs
Publication statusPublished - 2005

Keywords

  • Adaptation
  • Confidence measure
  • OOV rejection
  • Speech recognition

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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