Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge
Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed, Shah Nawaz, Rohan Kumar Das, Muhammad Zaigham Zaheer, Junaid Mir, Muhammad Haroon Yousaf, Khalid Mahmood Malik, Markus Schedl
TL;DR
This paper discusses the FAME 2026 Challenge, which aims to develop face-voice association methods effective across different languages, addressing multilingual communication scenarios.
Contribution
It introduces a new challenge focused on cross-lingual face-voice association, highlighting the need for models that generalize across languages.
Findings
Challenge fosters research on multilingual face-voice matching
Preliminary results show significant cross-lingual performance gaps
Encourages development of language-agnostic models
Abstract
Over half of the world's population is bilingual and people often communicate under multilingual scenarios. The Face-Voice Association in Multilingual Environments (FAME) 2026 Challenge, held at ICASSP 2026, focuses on developing methods for face-voice association that are effective when the language at test-time is different than the training one. This report provides a brief summary of the challenge.
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Taxonomy
TopicsFace recognition and analysis · Face Recognition and Perception · Phonetics and Phonology Research