Feature recovery for noise-robust speaker verification

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.