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The challenge provides a benchmark dataset, pretrained baseline models, and an evaluation framework to advance face--voice association, highlighting the need to foster the development of models that capture identity-specific aspects beyond language and gender.
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.
