Pseudo speaker models for text-independent speaker verification using rank threshold

A novel speaker verification method is proposed that utilizes pseudo speaker models in the speaker ranking selection (SRS) method. SRS is a recently proposed method that has been shown to give higher performance than the traditional T-norm method. However, the superior performance of SRS is based on utilizing a large number of background speaker models. When enough number of speakers is not available for the background models, the performance of SRS significantly degrades. To achieve higher performance with SRS even when only a small number of speakers are available, we propose to augment the set of background models by adding pseudo speaker models (PSMs). Text-independent speaker verification experiments are performed using a large scale corpus designed for speaker recognition constructed by National Research Institute of Police Science (NRIPS) in Japan. It is shown that the proposed SRS based system with PSMs gives 0.29% equal error rate, which is lower than 0.46% by the original SRS. The minimum DCF scores by the proposed and the original methods are 0.14 and 0.63, respectively.