Unsupervised Feature Enhancement for speaker verification [article]

Phani Sankar Nidadavolu, Saurabh Kataria, Jesús Villalba, Paola García-Perera, Najim Dehak
2020 arXiv   pre-print
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
more » ... al, 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.
arXiv:1910.11915v2 fatcat:rcih4ckssbhqriuoedx6l6yeqy