arXiv:2606. 31411v1 Announce Type: cross Abstract: Rapid advancements in generative speech technology have compromised the reliability of voice biometrics.
By Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans
arXiv:2607. 14753v1 Announce Type: cross Abstract: Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV).
By Sofya Savelyeva, Mariia Perunova, Evgeny Kushnir, Artem Dvirniak, Dmitrii Korzh, Oleg Y. Rogov
arXiv:2606. 14639v1 Announce Type: cross Abstract: Recent advances in speech generation have significantly improved the naturalness of synthetic speech, making spoofing detection increasingly challenging.
By Hugo Daumain, Driss Matrouf, Khaled Khelif, Mickael Rouvier
arXiv:2509. 14959v3 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and anti-spoofing countermeasure (CM) systems.
By Anton Selitskiy, Akib Shahriyar, Jishnuraj Prakasan
arXiv:2606. 16837v1 Announce Type: cross Abstract: Spoofed speech detection is increasingly challenged by realistic synthesis, voice conversion, and replay attacks, with cross-dataset generalization remaining a major limitation.
By Mahtab Masoudi Nezhad, Nima Karimian
The paper introduces GUARD, a lightweight speaker identity unlearning framework designed to prevent re-identification in zero-shot text-to-speech systems. GUARD employs a learned speaker gate and speaker-agnostic activation steering on a frozen TTS backbone, optimizing steering vectors through group-relative reward optimization to reduce similarity to forgotten speakers while maintaining intelligibility and naturalness. Experiments on CosyVoice2 show that GUARD significantly lowers forget-speaker similarity and re-identification accuracy while preserving the ability to reproduce retained speakers.
By Hyoeun Kim, Yujun Lee, Kyuhong Shim