# Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan

arXiv: https://arxiv.org/abs/2404.09342 · HTML: https://mlsanity.com/paper/2404.09342/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Seeing Voices and Hearing Faces: Cross-modal biometric matching](https://mlsanity.com/paper/1804.00326/) (2018)
- [Learnable PINs: Cross-Modal Embeddings for Person Identity](https://mlsanity.com/paper/1805.00833/) (2018)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [Fusion and Orthogonal Projection for Improved Face-Voice Association](https://mlsanity.com/paper/2112.10483/) (2021)
- [Cross-modal Speaker Verification and Recognition: A Multilingual Perspective](https://mlsanity.com/paper/2004.13780/) (2020)
- [Audio-visual Speaker Recognition with a Cross-modal Discriminative Network](https://mlsanity.com/paper/2008.03894/) (2020)
- [Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signals](https://mlsanity.com/paper/1909.08685/) (2019)
- [Single-branch Network for Multimodal Training](https://mlsanity.com/paper/2303.06129/) (2023)
- [Speaker Recognition in Realistic Scenario Using Multimodal Data](https://mlsanity.com/paper/2302.13033/) (2023)

## Built on by (most cited first, 4 of 4)

- [Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan](https://mlsanity.com/paper/2508.04592/) (2025)
- [Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge](https://mlsanity.com/paper/2512.20376/) (2025)
- [Exploring Robust Face-Voice Matching in Multilingual Environments](https://mlsanity.com/paper/2407.19875/) (2024)
- [Contrastive Learning-based Chaining-Cluster for Multilingual Voice-Face Association](https://mlsanity.com/paper/2408.02025/) (2024)

## Abstract (arXiv)

The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, the audio-visual systems are one of the widely used multimodal systems. In the recent years, associating face and voice of a person has gained attention due to presence of unique correlation between them. The Face-voice Association in Multilingual Environments (FAME) Challenge 2024 focuses on exploring face-voice association under a unique condition of 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 scenario. The challenge uses a dataset namely, Multilingual Audio-Visual (MAV-Celeb) for exploring face-voice association in multilingual environments. This report provides the details of the challenge, dataset, baselines and task details for the FAME Challenge.