# Joint Optimization of ASV and CM tasks: BTUEF Team's Submission for WildSpoof Challenge

arXiv: https://arxiv.org/abs/2602.01722 · HTML: https://mlsanity.com/paper/2602.01722/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Anti-spoofing and adversarial audio attacks, one of 11 directions in Speech and audio language models.
- Builds on 10 of its 12 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Placed in

- Speech and audio language models / [Anti-spoofing and adversarial audio attacks](https://mlsanity.com/speech/anti-spoofing-and-adversarial-audio-attacks/)

## Builds on (most cited first, 10 of 10)

- [ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale](https://mlsanity.com/paper/2408.08739/) (2024)
- [The Attacker's Perspective on Automatic Speaker Verification: An Overview](https://mlsanity.com/paper/2004.08849/) (2020)
- [SASV 2022: The First Spoofing-Aware Speaker Verification Challenge](https://mlsanity.com/paper/2203.14732/) (2022)
- [a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification](https://mlsanity.com/paper/2403.01355/) (2024)
- [Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis](https://mlsanity.com/paper/2406.10836/) (2024)
- [Replay Attacks Against Audio Deepfake Detection](https://mlsanity.com/paper/2505.14862/) (2025)
- [WildSpoof Challenge Evaluation Plan](https://mlsanity.com/paper/2508.16858/) (2025)
- [Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification](https://mlsanity.com/paper/2510.01818/) (2025)
- [Optimizing a-DCF for Spoofing-Robust Speaker Verification](https://mlsanity.com/paper/2407.04034/) (2024)
- [Spoofing-Robust Speaker Verification Using Parallel Embedding Fusion: BTU Speech Group's Approach for ASVspoof5 Challenge](https://mlsanity.com/paper/2408.15877/) (2024)

## Abstract (arXiv)

Spoofing-aware speaker verification (SASV) jointly addresses automatic speaker verification and spoofing countermeasures to improve robustness against adversarial attacks. In this paper, we investigate our recently proposed modular SASV framework that enables effective reuse of publicly available ASV and CM systems through non-linear fusion, explicitly modeling their interaction, and optimization with an operating-condition-dependent trainable a-DCF loss. The framework is evaluated using ECAPA-TDNN and ReDimNet as ASV embedding extractors and SSL-AASIST as the CM model, with experiments conducted both with and without fine-tuning on the WildSpoof SASV training data. Results show that the best performance is achieved by combining ReDimNet-based ASV embeddings with fine-tuned SSL-AASIST representations, yielding an a-DCF of 0.0515 on the progress evaluation set and 0.2163 on the final evaluation set.