Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25406
Title: Stochastic feature transformation with divergence-based out-of-handset rejection for robust speaker verification
Authors: Mak, MW 
Tsang, CL
Kung, SY
Keywords: Divergence
EM algorithm
Feature transformation
Handset distortion
Robust speaker verification
Issue Date: 2004
Publisher: Hindawi Publishing Corporation
Source: Eurasip journal on applied signal processing, 2004, v. 2004, no. 4, p. 452-465 How to cite?
Journal: Eurasip Journal on Applied Signal Processing 
Abstract: The performance of telephone-based speaker verification systems can be severely degraded by linear and nonlinear acoustic distortion caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the distortion. Specifically, a Gaussian mixture model (GMM)-based handset selector is trained to identify the most likely handset used by the claimants, and then handset-specific stochastic feature transformations are applied to the distorted feature vectors. This paper also proposes a divergence-based handset selector with out-of-handset (OOH) rejection capability to identify the "unseen" handsets. This is achieved by measuring the Jensen difference between the selector's output and a constant vector with identical elements. The resulting handset selector is combined with the proposed feature transformation technique for telephone-based speaker verification. Experimental results based on 150 speakers of the HTIMIT corpus show that the handset selector, either with or without OOH rejection capability, is able to identify the "seen" handsets accurately (98.3% in both cases). Results also demonstrate that feature transformation performs significantly better than the classical cepstral mean normalization approach. Finally, by using the transformation parameters of the seen handsets to transform the utterances with correctly identified handsets and processing those utterances with unseen handsets by cepstral mean subtraction (CMS), verification error rates are reduced significantly (from 12.41% to 6.59% on average).
URI: http://hdl.handle.net/10397/25406
ISSN: 1110-8657
DOI: 10.1155/S1110865704308048
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