Sparse representation over learned and discriminatively learned dictionaries for speaker verification

In this work, a speaker verification (SV) method is proposed employing the sparse representation of GMM mean shifted supervectors over learned and discriminatively learned dictionaries. This work is motivated by recently proposed speaker verification methods employing the sparse representation classification (SRC) over exemplar dictionaries created from either GMM mean shifted supervectors or i-vectors. The proposed approach with discriminatively learned dictionary results in an equal error rate of 1.53 % which is found to be better than those of similar complexity SV systems developed using the i-vector based approach and the exemplar based SRC approaches with session/channel variability compensation on NIST 2003 SRE dataset.