An Initial Study of Physiological Markers for Deepfake Speech Detection
Conventional deepfake speech detectors primarily target synthesis artifacts and often degrade under unseen attacks or recording conditions. Human speech, however, contains physiological markers associated with respiration, phonation, and temporal coordination arising from the speech-production system that synthesizers may not reproduce faithfully. This motivates the hypothesis that deepfake speech can be detected by identifying inconsistencies in these patterns, which we test using 36 handcrafted physiological markers organized into respiratory, vocal, and temporal categories. We evaluate the marker vectors with logistic regression and SVM classifiers on pooled English recordings. The bes...
| Authors | Vojtěch Staněk, Michal Frič, Kamil Malinka et al. |
|---|---|
| Primary category | cs.SD |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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