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Automatization Noise-robust feature based on sparse representation for speaker recognition

The performance of speaker recognition suffers substantial degradation in noisy environments. To solve this problem, we propose a new feature extraction method based on sparse coding to improve the noise robustness of the speaker recognition. In this method, an over-complete dictionary, which is powerful in identifying transient underlying structures and harmonic periodicities, is trained with amounts of unlabeled data. Next, speech signals are sparsely represented by atoms of the dictionary. After that, the proposed feature is extracted from the sparse representation by a tuning function which simulates the hearing mechanism. Experiments show that the proposed method outperforms the mel-frequency cepstral coefficients (MFCC) and the perceptual linear predictive (PLP) feature in various noise conditions.