APSIPA 2025, 17th Asia Pacific Signal and Information Processing Association Annual Summit and Conference, 22-24 October 2025, Shangri-la, Singapore
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 semiinformed attack model, it significantly outperforms a competing approach to sex obfuscation.
Type:
Conference
City:
Shangri-la
Date:
2025-10-22
Department:
Digital Security
Eurecom Ref:
8317
Copyright:
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