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

Reference-free Adversarial Sex Obfuscation in Speech
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 cha…

Die ePetition 179278 an den Bundestag aus dem Bereich Internet mit dem Titel
Verpflichtung von zur Daseinsvorsorge gehörenden Unternehmen zum frei zugänglichen Online-Angebot ohne Kontozwang etc. von Dritten
kann bis zum Mittwoch 20. August 2025 01:00 UTC mitgezeichnet werden und lautet
Mit der Petition wird geforde…

High-gain model-following control for trajectory tracking
Nicals Tietze, Kai Wulff, Johann Reger
https://arxiv.org/abs/2506.13463 https://

High-gain model-following control for trajectory tracking
We consider trajectory tracking for minimum-phase nonlinear systems in Byrnes-Isidori form using the model-following control (MFC) architecture. The tracking problem is motivated by a hierarchical control concept where a higher-level instance provides the reference trajectory at run-time. We present a computational efficient implementation of the feedback linearisation MFC design, and apply high-gain feedback in the process control loop (PCL) to achieve practical tracking in presence of Lipschi…

The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation
Michele Panariello, Sarina Meyer, Pierre Champion, Xiaoxiao Miao, Massimiliano Todisco, Ngoc Thang Vu, Nicholas Evans
https://arxiv.org/abs/2507.22534

The Risks and Detection of Overestimated Privacy Protection in Voice Anonymisation
Voice anonymisation aims to conceal the voice identity of speakers in speech recordings. Privacy protection is usually estimated from the difficulty of using a speaker verification system to re-identify the speaker post-anonymisation. Performance assessments are therefore dependent on the verification model as well as the anonymisation system. There is hence potential for privacy protection to be overestimated when the verification system is poorly trained, perhaps with mismatched data. In this…