arXiv:2606. 10911v1 Announce Type: cross Abstract: Claims about the robustness and fairness of deepfake speech detectors are only as credible as the datasets used to train and evaluate those systems.
By Vojt\v{e}ch Stan\v{e}k, Eva Trnovsk\'a, Kamil Malinka, Anton Firc
arXiv:2606. 10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve.
By Ashley R. Keaton, Zahra Khanjani, Christine Mallinson, Vandana P. Janeja
arXiv:2607. 21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks.
By Daniyal Kabir Dar, Arun Ross
arXiv:2609.18533v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representation...
By Nicolas Bourrel, Abderrahmane Issam, Gerasimos Spanakis
arXiv:2609.17913v1 Announce Type: new
Abstract: Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead t...
By Marta Moscati, Swapnil Khandoker, Muhammad Saad Saeed, Shah Nawaz, Fatima Noor, Rohan Kumar Das, Mubashir Noman, Junaid Mir, Muhammad Haroon Yousaf, Khalid Malik, Markus Schedl
arXiv:2607. 28351v2 Announce Type: replace-cross Abstract: Speech deepfake detection has expanded in scope with increasingly heterogeneous spoofing mechanisms, including speech synthesis, voice conversion, vocoder reconstruction, and neural-codec resynthesis.
By Wan Lin, Li Wang, Jindong Wang, Kunyu Feng, Zhizheng Wu