{"ID":22918822,"CreatedAt":"2026-09-17T01:02:08.507062015Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.17913","arxiv_id":"2609.17913","title":"Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge Evaluation Plan","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 to a performance deterioration when the model has to identify a multilingual speaker or distinguis same-gender speakers. To investigate these issues, we introduce the Face-voice Association across LAnguages and Gender (FLAG) 2027 Challenge. The challenge formulates face--voice association as a cross-modal verification task: given a voice, identify the speaker's face from a ``gallery'' of faces consisting of the speaker's face and a set of negative samples. Models are evaluated on identities not present in the training data (``unseen'') and both for languages present or absent from the training data (``heard'' and ``unheard''). Two evaluation settings are used to test models' reliance on gender: a standard, unconstrained and a gender-constrained one, where the latter uses a same-gender gallery. The performance of existing, baseline models in these settings reveals that models performance degrades under language shifts and in gender-constrained settings, highlighting the need to foster the development of models that capture identity-specific aspects beyond language and gender. The challenge provides a benchmark dataset, pretrained baseline models, and an evaluation framework to advance face--voice association.","short_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 to a performance deterioration when the model has to identify a multilingual speaker or distinguis same-gender speakers. To investigate these issues, we introduce the Fac...","url_abs":"https://arxiv.org/abs/2609.17913","url_pdf":"https://arxiv.org/pdf/2609.17913v1","authors":"[\"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\"]","published":"2026-09-15T23:15:53Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}