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In this paper, we propose an automatic data-driven technique for selecting proper background dataset. By the technique, impostor confidence(IC) is proposed as a metric and more discriminative background dataset is automatically chose by impostor confidence(IC) to train more discriminative model. Experiment results on NIST 2008 SRE corpus in GMM-SVM speaker verification system show that the proposed...
Acoustic feature extraction from speech is a fundamental part in both automatic speech recognition and automatic speaker recognition. Mel-frequency cepstral coefficients (MFCCs) are widely used in both of the above two research directions. A new feature extraction technique named perceptual MVDR-based cepstral coefficients (PMCCs) has been demonstrated to perform superior in automatic speech recognition...
Recently, using maximum likelihood linear regression (MLLR) transforms as the features for SVM based speaker recognition has been proposed. This can achieve performance comparable to that obtained with state-of-the-art approaches. In this paper, we focus on calculating the transforms based on a GMM universal background model (UBM). Rather than estimating the transforms using maximum likelihood criterion,...
Gaussian mixture models with an universal background model (UBM) have been the standard method for speaker recognition. Typically, maximum a posteriori (MAP) or maximum likelihood linear regression (MLLR) is used to adapt the means of the UBM. Together with the SVM modeling technique, these approaches can achieve excellent performance. MLLR is quite efficient when the amount of adaptation data is...
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