A fast approach to psychoacoustic model compensation for robust speaker recognition in additive noise

This paper addresses the problem of speaker verification in the presence of additive noise. We propose a fast implementation of Psychoacoustic Model Compensation (Psy-Comp) scheme for static features along with model domain mean and variance normalization for robust speaker recognition in noisy conditions. The proposed algorithms are validated through experiments on noise corrupted NIST-2000 speaker recognition database. We show that the Psy-Comp scheme along with model domain mean and variance normalization provide significant performance gain compared to the Vector Taylor Series (VTS) scheme and feature domain cepstral mean and variance normalization scheme. Moreover, the computational cost of the proposed method is significantly less than the VTS scheme.

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