Unsupervised Feature Enhancement for speaker verification
This work addresses robustness in speaker verification systems for applications in noisy environments, but it is incremental as it builds on existing enhancement and augmentation methods.
The authors tackled the problem of making speaker verification robust to adverse scenarios by developing an unsupervised feature enhancement approach in the log-filter bank domain, which yielded significant improvements on real and simulated noisy and reverberant test sets when data augmentation was not used or was limited, and slight improvements when full augmentation was applied.
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.