Impostor detection in speaker recognition using confusion-based confidence measures

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In this letter we introduce confusion-based confidence measures for detecting an impostor in speaker recognition, which does not require an alternative hypothesis. Most traditional speaker verification methods are based on a hypothesis test, and their performance depends on the robustness of an alternative hypothesis. Compared with the conventional Gaussian mixture model-universal background model (GMM-UBM)) scheme, our confusion-based measures show better performance in noise-corrupted speech. The additional computational requirements for our methods are negligible when used to detect or reject impostors.
Publisher
ELECTRONICS TELECOMMUNICATIONS RESEARCH INST
Issue Date
2006-12
Language
English
Article Type
Article
Keywords

MODELS; IDENTIFICATION

Citation

ETRI JOURNAL, v.28, no.6, pp.811 - 814

ISSN
1225-6463
URI
http://hdl.handle.net/10203/23068
Appears in Collection
EE-Journal Papers(저널논문)
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