Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18787
Title: Utterance partitioning with acoustic vector resampling for GMM-SVM speaker verification
Authors: Mak, MW 
Rao, W
Keywords: Data imbalance
GMM-supervectors (GSV)
GMM-SVM
Random resampling
Speaker verification
Support vector machine
Utterance partitioning
Issue Date: 2011
Publisher: Elsevier Science Bv
Source: Speech communication, 2011, v. 53, no. 1, p. 119-130 How to cite?
Journal: Speech Communication 
Abstract: Recent research has demonstrated the merit of combining Gaussian mixture models and support vector machine (SVM) for text-independent speaker verification. However, one unaddressed issue in this GMM-SVM approach is the imbalance between the numbers of speaker-class utterances and impostor-class utterances available for training a speaker-dependent SVM. This paper proposes a resampling technique - namely utterance partitioning with acoustic vector resampling (UP-AVR) - to mitigate the data imbalance problem. Briefly, the sequence order of acoustic vectors in an enrollment utterance is first randomized, which is followed by partitioning the randomized sequence into a number of segments. Each of these segments is then used to produce a GMM supervector via MAP adaptation and mean vector concatenation. The randomization and partitioning processes are repeated several times to produce a sufficient number of speaker-class supervectors for training an SVM. Experimental evaluations based on the NIST 2002 and 2004 SRE suggest that UP-AVR can reduce the error rate of GMM-SVM systems.
URI: http://hdl.handle.net/10397/18787
DOI: 10.1016/j.specom.2010.06.011
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