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The total variability i-vector based speaker verification system is one of the most successful systems in the recent NIST evaluations. It achieves significant improvement in performance over the conventional GMM-UBM based systems by using the projections of the GMM mean shifted supervectors to a low dimensional space for representation. This low dimensional projections are commonly referred to as...
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...
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