Unsupervised Feature Enhancement for Speaker Verification

Abstract

The task of making speaker verification systems robust to adverse scenarios remains a challenging and an active area of research. We developed an unsupervised feature enhancement approach in log-filter bank space with the end goal of improving speaker verification performance. We experimented with using both real speech recorded in adverse environments and degraded speech obtained by simulation to train the enhancement systems. The effectiveness of this approach was shown by testing on several real, simulated noisy, and reverberant test sets. The approach yielded significant improvements on both real and simulated sets when data augmentation was not used in speaker verification pipeline. We also experimented with training the x-vector and PLDA systems with enhanced augmented features instead of augmented features and observed better performance on real test conditions (4.2% relative improvement in minDCF on SRI).