  The promising performance of Deep Learning (DL) in speech recognition has
motivated the use of DL in other speech technology applications such as speaker
recognition. Given i-vectors as inputs, the authors proposed an impostor
selection algorithm and a universal model adaptation process in a hybrid system
based on Deep Belief Networks (DBN) and Deep Neural Networks (DNN) to
discriminatively model each target speaker. In order to have more insight into
the behavior of DL techniques in both single and multi-session speaker
enrollment tasks, some experiments have been carried out in this paper in both
scenarios. Additionally, the parameters of the global model, referred to as
universal DBN (UDBN), are normalized before adaptation. UDBN normalization
facilitates training DNNs specifically with more than one hidden layer.
Experiments are performed on the NIST SRE 2006 corpus. It is shown that the
proposed impostor selection algorithm and UDBN adaptation process enhance the
performance of conventional DNNs 8-20 % and 16-20 % in terms of EER for the
single and multi-session tasks, respectively. In both scenarios, the proposed
architectures outperform the baseline systems obtaining up to 17 % reduction in
EER.
