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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.
