Bayesian confidence scoring and adaptation techniques for speech recognition

Tae Yoon Kim; Hanseok Ko

doi:10.1093/ietcom/e88-b.4.1756

Bayesian confidence scoring and adaptation techniques for speech recognition

Tae Yoon Kim, Hanseok Ko

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	1756-1759
Number of pages	4
Journal	IEICE Transactions on Communications
Volume	E88-B
Issue number	4
DOIs	https://doi.org/10.1093/ietcom/e88-b.4.1756
Publication status	Published - 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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10.1093/ietcom/e88-b.4.1756

Cite this

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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.",

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AB - 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.

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KW - Confidence measure

KW - OOV rejection

KW - Speech recognition

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Bayesian confidence scoring and adaptation techniques for speech recognition

Abstract

Keywords

ASJC Scopus subject areas

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