Improving GMMUBM speaker verification using discriminative feedback adaptation
The Gaussian mixture model  Universal background model (GMMUBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle unseen acoustic patterns. However, since GMMUBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors voices that are similar to the target speakers voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti-model, while preserving the generalization ability of the GMMUBM approach. This is achieved by adapting the UBM to a target speaker dependent anti-model based on a minimum verification squared-error criterion, rather than estimating the model from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMMUBM approach.
speaker verification discriminative feedback adaptation minimum verification squared-error loglikelihood ratio