# On Learning Associations of Faces and Voices

arXiv: https://arxiv.org/abs/1805.05553 · HTML: https://mlsanity.com/paper/1805.05553/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 26 of 26)

- [VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency](https://mlsanity.com/paper/2101.03149/) (2021)
- [Neural Dubber: Dubbing for Videos According to Scripts](https://mlsanity.com/paper/2110.08243/) (2021)
- [MAAS: Multi-modal Assignation for Active Speaker Detection](https://mlsanity.com/paper/2101.03682/) (2021)
- [Fusion and Orthogonal Projection for Improved Face-Voice Association](https://mlsanity.com/paper/2112.10483/) (2021)
- [Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association](https://mlsanity.com/paper/2103.07293/) (2021)
- [Look&Listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech Enhancement](https://mlsanity.com/paper/2203.02216/) (2022)
- [Self-Supervised 3D Face Reconstruction via Conditional Estimation](https://mlsanity.com/paper/2110.04800/) (2021)
- [Strumming to the Beat: Audio-Conditioned Contrastive Video Textures](https://mlsanity.com/paper/2104.02687/) (2021)
- [Single-branch Network for Multimodal Training](https://mlsanity.com/paper/2303.06129/) (2023)
- [A cappella: Audio-visual Singing Voice Separation](https://mlsanity.com/paper/2104.09946/) (2021)
- [Speaker Recognition in Realistic Scenario Using Multimodal Data](https://mlsanity.com/paper/2302.13033/) (2023)
- [Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?](https://mlsanity.com/paper/2203.09824/) (2022)
- [Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast](https://mlsanity.com/paper/2204.14057/) (2022)
- [Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations](https://mlsanity.com/paper/2107.12003/) (2021)
- [FaVoA: Face-Voice Association Favours Ambiguous Speaker Detection](https://mlsanity.com/paper/2109.00577/) (2021)
- [Audio-Visual Speaker Verification via Joint Cross-Attention](https://mlsanity.com/paper/2309.16569/) (2023)
- [Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach](https://mlsanity.com/paper/2408.07445/) (2024)
- [Hear Your Face: Face-based voice conversion with F0 estimation](https://mlsanity.com/paper/2408.09802/) (2024)
- [Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices](https://mlsanity.com/paper/2104.10299/) (2021)
- [Controlled AutoEncoders to Generate Faces from Voices](https://mlsanity.com/paper/2107.07988/) (2021)
- [Learning Branched Fusion and Orthogonal Projection for Face-Voice Association](https://mlsanity.com/paper/2208.10238/) (2022)
- [Chameleon: Images Are What You Need For Multimodal Learning Robust To Missing Modalities](https://mlsanity.com/paper/2407.16243/) (2024)
- [Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors](https://mlsanity.com/paper/2208.08118/) (2022)
- [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)
- [Face and Voice Cross-modal Association with Learning Convex Feature Embedding](https://mlsanity.com/paper/2607.28129/) (2026)

## Abstract (arXiv)

In this paper, we study the associations between human faces and voices. Audiovisual integration, specifically the integration of facial and vocal information is a well-researched area in neuroscience. It is shown that the overlapping information between the two modalities plays a significant role in perceptual tasks such as speaker identification. Through an online study on a new dataset we created, we confirm previous findings that people can associate unseen faces with corresponding voices and vice versa with greater than chance accuracy. We computationally model the overlapping information between faces and voices and show that the learned cross-modal representation contains enough information to identify matching faces and voices with performance similar to that of humans. Our representation exhibits correlations to certain demographic attributes and features obtained from either visual or aural modality alone. We release our dataset of audiovisual recordings and demographic annotations of people reading out short text used in our studies.