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.
PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake De- tection and Naturalness Evaluation
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Phoneme-level analysis using self-supervised embeddings identifies higher divergence in complex vowels and fricatives for emotional voice conversion deepfakes, enabling more interpretable detection across emotions.
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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.
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Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Phoneme-level analysis using self-supervised embeddings identifies higher divergence in complex vowels and fricatives for emotional voice conversion deepfakes, enabling more interpretable detection across emotions.