An Initial Study of Physiological Markers for Deepfake Speech Detection
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arXiv:2607. 21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks.
arXiv:2607. 28351v2 Announce Type: replace-cross Abstract: Speech deepfake detection has expanded in scope with increasingly heterogeneous spoofing mechanisms, including speech synthesis, voice conversion, vocoder reconstruction, and neural-codec resynthesis.
arXiv:2606. 27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.
arXiv:2509.10452v3 Announce Type: replace-cross Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance....
The study explores acoustic biomarkers derived from spontaneous speech to differentiate Alzheimer's disease patients from healthy controls. Using a structured picture‑description task, low‑level prosodic, spectral, and temporal features were extracted and fed into various machine learning classifiers. The best models achieved clinically meaningful separation between diagnostic groups, indicating that non‑invasive audio screening could aid early detection of dementia.
arXiv:2608.23363v1 Announce Type: cross Abstract: Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deep...