Kurnaz, OguzhanMishra, JagabandhuKinnunen, Tomi H.Hanilci, Cemal2026-02-082026-02-0820251070-99081558-2361https://doi.org/10.1109/LSP.2025.3545290https://hdl.handle.net/20.500.12885/5905Automatic 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).eninfo:eu-repo/semantics/closedAccessMeasurementCostsTrainingCost functionSignal processing algorithmsError analysisSecurityRobustnessComputer architectureTraining dataa-DCFspoofing-robust speaker verificationArticle10.1109/LSP.2025.35452903210811085WOS:0014450579000022-s2.0-105001060545Q2Q1