In this paper, we present our system submitted to the Spoofing Aware Speaker Verification Challenge (SASVC) 2022. Our submission focuses on bridging the gap between automatic speaker verification (ASV) and countermeasure (CM) systems. We introduce a general norm-constrained score-level ensemble method that can improve robustness to zero-effort impostors and spoofing attacks by jointly processing the scores extracted from the ASV and CM subsystems. Furthermore, we explore that the ensemble system can provide better performance when both ASV and CM subsystems are optimized. Experimental results show that our primary system yields 0.45% SV-EER, 0.26% SPF-EER, and 0.37% SASV-EER on the SASVC 2022 evaluation set. The relative improvements are 96.08%, 66.67%, and 94.19% over the best official baseline, respectively. All of our code and pre-trained model weights are publicly available and reproducible.
Cite as: Zhang, P., Hu, P., Zhang, X. (2022) Norm-constrained Score-level Ensemble for Spoofing Aware Speaker Verification. Proc. Interspeech 2022, 4371-4375, doi: 10.21437/Interspeech.2022-470
@inproceedings{zhang22f_interspeech,
author={Peng Zhang and Peng Hu and Xueliang Zhang},
title={{Norm-constrained Score-level Ensemble for Spoofing Aware Speaker Verification}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={4371--4375},
doi={10.21437/Interspeech.2022-470}
}