Speaker adaptive confidence scoring using Bayesian combining

Tae Yoon Kim, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. If the joint pdf in the two classes are correctly estimated, this method guarantees an optimal combining in the minimum Bayes risk sense. Investigating the distribution of confidence features, we found out that the pdfs are well estimated by Gaussian mixture model with full covariance matrix in combining small number of features. In addition, the adaptation of a confidence score by adapting the joint pdf is presented. The proposed methods reduced the classification error rate by 17% from the conventional single feature based confidence scoring method in isolated word Out-of-Vocabulary rejection test.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeI
DOIs
Publication statusPublished - 2005 Dec 1
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 2005 Mar 182005 Mar 23

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period05/3/1805/3/23

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

Cite this

Kim, T. Y., & Ko, H. (2005). Speaker adaptive confidence scoring using Bayesian combining. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. I). [1415054] https://doi.org/10.1109/ICASSP.2005.1415054