arXiv Computer Vision 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

Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge Evaluation Plan

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arXiv AI
Aug 6

MERaLiON-GR: Speech Gender Recognition Model for English and SEA Languages

arXiv:2608. 04433v1 Announce Type: cross Abstract: We present MERaLiON-GR, a speech gender recognition system that performs binary classification (female / male) on English and Southeast Asian (SEA) languages.

By Qiongqiong Wang, Ai Ti Aw, Nancy F. Chen, Ying Lay Chiu, Yang Ding, Yingxu He, Ridong Jiang, Zhuohan Liu, Yanfeng Lu, Yi Ma, Muhammad Huzaifah, Nabilah Binte Md Johan, Nattadaporn Lertcheva, Pham Minh Duc, Sailor Hardik Bhupendra, Siti Umairah Binte Mohammad Salleh, Shuo Sun, Tarun Kumar Vangani, Jeremy H. M. Wong, Jinyang Wu, Longyin Zhang
arXiv AI
Jun 10

Linguistically Augmented Audio Speech Data (LinguAS)

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 Computation and Language
Oct 16

Bias Mitigation in Audio-Enabled LLMs through Adversarial Training and Distillation Methods

The paper presents methods to reduce gender bias in audio‑enabled large language models (AuLLMs) by using adversarial training to unlearn gender perception without harming performance on other tasks. It also introduces a distillation approach that retains gender awareness but aligns responses with a gender‑agnostic teacher unless explicitly prompted. Experiments on three AuLLMs show promising results for gender‑unlearning and partial success for the distillation technique.

By Adnan Al Ali, Jind\v{r}ich Libovick\'y, Anne Lauscher