Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. We propose a spoofing-robust ASV system optimized directly for the recently introduced architecture-agnostic detection cost function (a-DCF), which allows targeting a desired trade-off between the contradicting aims of user convenience and robustness to spoofing. We combine a-DCF and binary cross-entropy (BCE) with a novel straightforward threshold optimization technique. Our results with an embedding fusion system on ASVspoof2019 data demonstrate relative improvement of 13% over a system trained using BCE only (from minimum a-DCF of 0.1445 to 0.1254). Using an alternative non-linear score fusion approach provides relative improvement of 43% (from minimum a-DCF of 0.0508 to 0.0289).
@article{arxiv.2407.04034,
title = {Optimizing a-DCF for Spoofing-Robust Speaker Verification},
author = {Oğuzhan Kurnaz and Jagabandhu Mishra and Tomi H. Kinnunen and Cemal Hanilçi},
journal= {arXiv preprint arXiv:2407.04034},
year = {2025}
}