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The results imply that existing deepfake speech detection methods are insufficient for reliably detecting edit-based speech deepfakes in various scenarios unseen during training.
Quantifying the divergence between real and synthetic phoneme distributions via Kullback-Leibler divergence can pinpoint the most vulnerable phonemes for detecting audio deepfakes.
Even when overall accuracy seems balanced, audio deepfake detection models can exhibit significant gender bias, masked by standard metrics like EER.
