Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan

TL;DR

The FAME 2026 Challenge evaluates face-voice association in multilingual settings using the MAV-Celeb dataset, aiming to advance multimodal biometric systems in diverse linguistic environments.

Contribution

This work introduces a new challenge and dataset focused on face-voice association in multilingual scenarios, addressing a gap in current multimodal biometric research.

Findings

Baseline models established for face-voice association in multilingual environments

Detailed challenge and dataset description provided

Initial results demonstrate the feasibility of face-voice matching across languages

Abstract

The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, audio-visual systems are among the most widely used multimodal systems. In the recent years, associating face and voice of a person has gained attention due to the presence of unique correlation between them. The Face-voice Association in Multilingual Environments (FAME) 2026 Challenge focuses on exploring face-voice association under the unique condition of a multilingual scenario. This condition is inspired from the fact that half of the world's population is bilingual and most often people communicate under multilingual scenarios. The challenge uses a dataset named Multilingual Audio-Visual (MAV-Celeb) for exploring face-voice association in multilingual environments. This report provides the details of the challenge, dataset, baseline models, and task details for the FAME Challenge.