Speaker recognition based on discriminative feature extraction - optimization of mel-cepstral features using second-order all-pass warping function

This paper describes a new framework for designing speaker recognition systems based on the discriminative feature extraction (DFE) method. We apply a mel-cepstral estimation technique to the feature extractor in a Gaussian mixture model (GMM)-based text-independent speaker identification system. The mel-cepstral estimation technique uses the second-order all-pass warping function for frequency transformation. We jointly optimize the frequency warping parameters of the feature extractor and the GMM parameters of the classifier based on a minimum classification error (MCE) criterion. Experimental results show that the frequency warped scale after optimization is different from traditional linear/mel scales; moreover, the proposed system outperforms conventional systems trained with the generalized probabilistic descent (GPD) method in which only the classifier is optimized.

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