Speaker verification using sparse representation over KSVD learned dictionary

In this work, we explore the use of sparse representation of GMM mean shifted supervectors over a learned dictionary for the speaker verification (SV) task. In this method the dictionaries are learned using the KSVD algorithm unlike the recently proposed SV methods employing the sparse representation classification (SRC) over exemplar dictionaries. The proposed approach with learned dictionary results in an equal error rate of 1.56 % on NIST 2003 SRE dataset, which is found to be better than those of the state-of-the-art i-vector based approach and the exemplar based SRC approaches using either GMM mean shifted supervectors or i-vectors, with appropriate session/channel variability compensation techniques applied.