Abstract
Noisy condition is an important extrinsic degradation affecting speaker verification system performance. A feature-recovery approach is proposed to eliminate noise-dependent variability in feature space. A frame of the noisy feature vector is recovered using the information of itself and the neighbour feature vectors. Experiments are conducted on noisy test sets for text-dependent speaker verification tasks and the results indicate that the system can achieve significant performance improvement by using recovered feature vectors.
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