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Audio-language models struggle significantly with hidden evaluation tasks, with accuracy plummeting by nearly 12 percentage points on average, highlighting the challenge of true audio comprehension.
Existing audio deepfake detectors are sitting ducks outside the lab: AT-ADD introduces a challenge to push research towards real-world robustness and generalization across all audio types.
