  The task of making speaker verification systems robust to adverse scenarios
remain a challenging and an active area of research. We developed an
unsupervised feature enhancement approach in log-filter bank domain 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
the 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 or augmentation was used only during x-vector training.
When data augmentation was used for x-vector and PLDA training, our enhancement
approach yielded slight improvements.
