# BUT Systems and Analyses for the ASVspoof 5 Challenge

arXiv: https://arxiv.org/abs/2408.11152 · HTML: https://mlsanity.com/paper/2408.11152/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 12 of 12)

- [SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis](https://mlsanity.com/paper/2508.07944/) (2025)
- [Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors](https://mlsanity.com/paper/2604.01330/) (2026)
- [An Intervention-Based Framework for Shortcut Diagnosis in Spoofing Countermeasures](https://mlsanity.com/paper/2607.03150/) (2026)
- [Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System](https://mlsanity.com/paper/2508.20983/) (2025)
- [A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection](https://mlsanity.com/paper/2603.01482/) (2026)
- [RAT: Reference-Augmented Training for ASV Anti-Spoofing](https://mlsanity.com/paper/2606.10908/) (2026)
- [What Do Deepfake Speech Detectors Actually Hear?](https://mlsanity.com/paper/2606.10912/) (2026)
- [SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing](https://mlsanity.com/paper/2606.11674/) (2026)
- [Supervised Post-training of Speech Foundation Models for Robust Adaptation in Speech Deepfake Detection](https://mlsanity.com/paper/2606.25328/) (2026)
- [QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2610.16390/) (2026)
- [Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors](https://mlsanity.com/paper/2610.16826/) (2026)
- [An Initial Study of Physiological Markers for Deepfake Speech Detection](https://mlsanity.com/paper/2610.18934/) (2026)

## Abstract (arXiv)

This paper describes the BUT submitted systems for the ASVspoof 5 challenge, along with analyses. For the conventional deepfake detection task, we use ResNet18 and self-supervised models for the closed and open conditions, respectively. In addition, we analyze and visualize different combinations of speaker information and spoofing information as label schemes for training. For spoofing-robust automatic speaker verification (SASV), we introduce effective priors and propose using logistic regression to jointly train affine transformations of the countermeasure scores and the automatic speaker verification scores in such a way that the SASV LLR is optimized.