Probing Speaker Identity Sensitivity in Audio Deepfake Detectors
Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks. Yet the same detector that achieves less than 1% error on one dataset can see its error rate increase twentyfold when evaluated on a different dataset. We argue that one contributing factor is speaker-identity reliance: standard training corpora correlate speaker identity with the genuine/synthetic label, allowing detectors to partially rely on speaker-related cues rather than synthesis artifacts alone. We propose the Identity Sensitivity Score (ISS), a per-utterance diagnostic that quantifies how much a detector's output changes across different speaker ...
| Authors | Daniyal Kabir Dar, Arun Ross |
|---|---|
| Primary category | cs.SD |
| Published | 23 July 2026 |
| Last updated at source | 7 September 2026 |
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