Sparsity based robust speaker identification using a discriminative dictionary learning approach

Speaker identification is a key component in many practical applications and the need of finding algorithms, which are robust under adverse noisy conditions, is extremely important. In this paper, the problem of text-independent speaker identification is studied in light of classification based on sparsity representation combined with a discriminative dictionary learning technique. Experimental evaluations on a small dataset reveal that the proposed method achieves a superior performance under short training sessions restrictions. In specific, the proposed method achieved high robustness for all the noisy conditions that were examined, when compared with a GMM universal background model (UBM-GMM) and sparse representation classification (SRC) approaches.