mlsanityData to 7 Oct 2026

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

arXiv 2404.09342 ↗First posted 14 Apr 2024cs.CV cs.SD eess.AScited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 14 Apr 2024.

Builds on

9 of 15 references.

References · 9

  1. 2018Seeing Voices and Hearing Faces: Cross-modal biometric matchingcited by 60
  2. 2018Learnable PINs: Cross-Modal Embeddings for Person Identitycited by 60
  3. 2019Utterance-level Aggregation For Speaker Recognition In The Wildcited by 49
  4. 2021Fusion and Orthogonal Projection for Improved Face-Voice Associationcited by 29
  5. 2020Cross-modal Speaker Verification and Recognition: A Multilingual Perspectivecited by 26
  6. 2020Audio-visual Speaker Recognition with a Cross-modal Discriminative Networkcited by 24
  7. 2019Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signalscited by 20
  8. 2023Single-branch Network for Multimodal Trainingcited by 13
  9. 2023Speaker Recognition in Realistic Scenario Using Multimodal Datacited by 9

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Citing papers per year: 2024: 2; 2025: 2.

4 papers.