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Current speech deepfake detection systems falter dramatically against emotionally expressive attacks, with performance dropping to near-random levels on the new AffectDF benchmark.
Finally, voice anonymization offers a smooth, tunable knob to balance privacy and prosody, instead of forcing you to pick just one.
Training on real speech prosody alone can cut speech deepfake error rates by over 70% on emotional attacks, a blindspot for current detectors.
Control the emotional tone of generated speech without any training by directly manipulating specific neurons within large audio-language models.
