A boosting approach for utterance verification

Authors:
Chengyu Dong;Yuan Dong;Dezhi Huang;Jun Guo;Haila Wang
Affiliations:
School of Information Engineering, Beijing University of Posts and Telecommunications, P.R. China;School of Information Engineering, Beijing University of Posts and Telecommunications, P.R. China and France Telecom R&D Beijing Co, Ltd., Beijing, P.R. China;France Telecom R&D Beijing Co, Ltd., Beijing, P.R. China;School of Information Engineering, Beijing University of Posts and Telecommunications, P.R. China;France Telecom R&D Beijing Co, Ltd., Beijing, P.R. China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

Utterance verification is a process, in which a spoken utterance is verified against the given keyword. This process is used to make a decision on acceptance or rejection. In this paper, we propose a new approach to the utterance verification, using a boosting classifier with ten confidence measures. This classifier combines a set of 'weak' learners into a 'strong' one. The experimental results present that it can remarkably improve the verification performance. Compared with a single confidence measure, the equal error rate is reduced by up to 23%. The results also show that the boosting classifier is better than the SVM and MLP classifiers, in term of the equal error rate.