In this paper, we propose to discriminatively model target and impostor spectral features using Deep Belief Networks (DBNs) for speaker recognition. In the feature level, the number of impostor samples is considerably large compared to previous works based on i-vectors. Therefore, those i-vector based impostor selection algorithms are not computationally practical. On the other hand, the number of samples for each target speaker is different from one speaker to another which makes the training process more difficult. In this work, we take advantage of DBN unsupervised learning to train a global model, which will be referred to as Universal DBN (UDBN). Then we adapt this UDBN to the data of each target speaker. The evaluation is performed on the core test condition of the NIST SRE 2006 database and it is shown that the proposed architecture achieves more than 8% relative improvement in comparison to the conventional Multilayer Perceptron (MLP).

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