3 papers
cs.CR2026
Tracking the Trend in How Speech Synthesizers Deceive People
Milan Šalko, Anton Firc, Kamil Malinka +4
Advances in speech synthesis have made deepfake audio highly realistic. Earlier studies reported 70-80% human detection accuracy, but relied primarily on older synthesizers. We com…
cs.SD2026
What Do Deepfake Speech Detectors Actually Hear?
Vojtěch Staněk, Veronika Jirmusová, Anton Firc +3
Deepfake speech detectors often output a single score without explaining why an audio sample is flagged, where in the signal the evidence lies, or what cues drive the decision. We…
cs.SD2026
RAT: Reference-Augmented Training for ASV Anti-Spoofing
Vojtěch Staněk, Anton Firc, Jakub Reš +1
We introduce a spoofing countermeasure architecture conditioned on speaker-reference recordings, but observe that it converges to a solution that effectively ignores the reference…