Norm-constrained Score-level Ensemble for Spoofing Aware Speaker Verification

Abstract

In this paper, we present the Elevoc systems submitted to the Spoofing Aware Speaker Verification Challenge (SASVC) 2022. Our submissions focus on bridge the gap between the automatic speaker verification (ASV) and countermeasure (CM) systems. We investigate a general and efficient norm-constrained score-level ensemble method which jointly processes the scores extracted from ASV and CM subsystems, improving robustness to both zero-effect imposters and spoof-ing attacks. 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, and obtains more than 96.08%, 66.67% and 94.19% relative improvements over the best performing baseline systems on the SASVC 2022 evaluation set. All of our code and pre-trained models weights are publicly available and reproducible 1 .