Robust Speaker Recognition using SNR-Aware Subspace-Based Enhancement and Probabilistic SVMs

In this paper, we present a robust text-independent speaker recognition system. The proposed system mainly includes an SNR-aware subspace-based enhancement technique and probabilistic support vector machines (SVMs). First, we construct a perceptual filterbank from psycho-acoustic model and incorporate it with the subspace-based enhancement approach. The prior SNR of each subband within the perceptual filterbank is taken to decide the estimator's gain to effectively suppress environmental background noises. Next, this study uses probabilistic SVMs to identify the speaker from the enhanced speech. The superiority of the proposed system has been demonstrated by twenty speaker recognition from AURORA-2 database with in-car noises