Noise-robust speaker recognition using subband likelihoods and reliable-feature selection

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We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable-feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable-feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable-feature selection achieves better performance than the conventional feature recombination system with reliable-feature selection.
Publisher
ELECTRONICS TELECOMMUNICATIONS RESEARCH INST
Issue Date
2008-02
Language
English
Article Type
Article
Keywords

SCORE NORMALIZATION; MODELS

Citation

ETRI JOURNAL, v.30, no.1, pp.89 - 100

ISSN
1225-6463
URI
http://hdl.handle.net/10203/23730
Appears in Collection
EE-Journal Papers(저널논문)
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