Bayesian confidence scoring and adaptation techniques for speech recognition

Tae Yoon Kim, Hanseok Ko

    Research output: Contribution to journalArticlepeer-review

    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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