Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge Evaluation Plan
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to a performance deterioration when the model has...
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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.