A diagnosis-first framework for gender bias in audio deepfake detection identifies acoustic representation differences and feature leakage as sources, with per-gender threshold adjustment reducing unfairness by 54-75% without accuracy loss.
Acoustic analysis of speech
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Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
A diagnosis-first framework for gender bias in audio deepfake detection identifies acoustic representation differences and feature leakage as sources, with per-gender threshold adjustment reducing unfairness by 54-75% without accuracy loss.