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arXiv · 2508.02295

Reference-free Adversarial Sex Obfuscation in Speech

Yangyang Qu·Michele Panariello·Massimiliano Todisco·Nicholas Evans
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Abstract

Sex conversion in speech involves privacy risks from data collection and often leaves residual sex-specific cues in outputs, even when target speaker references are unavailable. We introduce RASO for Reference-free Adversarial Sex Obfuscation. Innovations include a sex-conditional adversarial learning framework to disentangle linguistic content from sex-related acoustic markers and explicit regularisation to align fundamental frequency distributions and formant trajectories with sex-neutral characteristics learned from sex-balanced training data. RASO preserves linguistic content and, even when assessed under a semi-informed attack model, it significantly outperforms a competing approach to sex obfuscation.

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Yangyang Qu, Michele Panariello, Massimiliano Todisco, Nicholas Evans. 2025-08-04. Reference-free Adversarial Sex Obfuscation in Speech. https://arxiv.org/abs/2508.02295

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