# Optimizing a-DCF for Spoofing-Robust Speaker Verification

arXiv: https://arxiv.org/abs/2407.04034 · HTML: https://mlsanity.com/paper/2407.04034/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 4 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification](https://mlsanity.com/paper/2510.01818/) (2025)
- [Spoofing-Robust Speaker Verification Using Parallel Embedding Fusion: BTU Speech Group's Approach for ASVspoof5 Challenge](https://mlsanity.com/paper/2408.15877/) (2024)
- [Joint Optimization of ASV and CM tasks: BTUEF Team's Submission for WildSpoof Challenge](https://mlsanity.com/paper/2602.01722/) (2026)

## Abstract (arXiv)

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).