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A multi-class MLLR kernel for SVM speaker recognition

Published in:
Proc. IEEE Int. Connf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008, pp. 4117-4120.

Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a Gaussian mixture model (GMM), and maximum likelihood linear regression (MLLR) adaptation is used to adapt the means of the UBM. Recent work has examined this setup for the case where a global MLLR transform is applied to all the mixture components of the GMM UBM. This work produced positive results that warrant examining this setup with multi-class MLLR adaptation, which groups the UBM mixture components into classes and applies a different transform to each class. This paper extends the MLLR/GMM framework to the multiclass case. Experiments on the NIST SRE 2006 corpus show that multi-class MLLR improves on global MLLR and that the proposed system?s performance is comparable with state of the art systems.
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Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a...

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A new kernel for SVM MLLR based speaker recognition

Published in:
INTERSPEECH, 27-31 August 2007.

Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a Gaussian mixture model (GMM), and maximum likelihood linear regression (MLLR) adaptation is used to adapt the means of the UBM. We examine two possible SVM feature expansions that arise in this context: the first, a GMM supervector is constructed by stacking the means of the adapted GMM, and the second consists of the elements of the MLLR transform. We examine several kernels associated with these expansions. We show that both expansions are equivalent given an appropriate choice of kernels. Experiments performed on the NIST SRE 2006 corpus clearly highlight that our choice of kernels, which are motivated by distance metrics between GMMs, outperform ad-hoc ones. We also apply SVM nuisance attribute projection (NAP) to the kernels as a form of channel compensation and show that, with a proper choice of kernel, we achieve results comparable to existing SVM based recognizers.
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Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a...

READ MORE