Partial deepfake speech is detected by scoring unnatural frame-to-frame changes in self-supervised audio embeddings, reaching 0.59% EER on PartialSpoof and 0.03% on HAD with utterance-level labels only.
Graph attention networks for anti-spoofing,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SD 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Frame-level Temporal Difference Learning for Partial Deepfake Speech Detection
Partial deepfake speech is detected by scoring unnatural frame-to-frame changes in self-supervised audio embeddings, reaching 0.59% EER on PartialSpoof and 0.03% on HAD with utterance-level labels only.