i-Vector Modeling with Deep Belief Networks for Multi-Session Speaker Recognition
|Omid Ghahabi and Javier Hernando|
In this paper we propose an impostor selection method for a Deep Belief Network (DBN) based system which models i-vectors in a multi-session speaker verification task. In the proposed method, instead of choosing a fixed number of most informative impostors, a threshold is defined according to the frequencies of impostors. The selected impostors are then clustered and the centroids are considered as the final impostors for target speakers. The system first trains each target speaker unsupervisingly by an adaptation method and then models discriminatively each target speaker using the impostor centroids and target i-vectors. The evaluation is performed on the NIST 2014 i-vector challenge database and it is shown that the proposed DBN-based system achieves 23% relative improvement of minDCF over the baseline system in the challenge.