Sparse Representation for Speaker Identification

We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an over complete dictionary using the GMM mean super vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database. We therefore propose to represent a given test utterance as a linear combination of all the training utterances, thereby generating a naturally sparse representation. Using this sparsity, the unknown vector of coefficients is computed via l₁-minimization which is also the sparsest solution. Ideally, the vector of coefficients so obtained has nonzero entries representing the class index of the given test utterance. Experiments have been conducted on the standard TIMIT database and a comparison with the state-of-art speaker identification algorithms yields a favorable performance index for the proposed algorithm.