# XM-ALIGN: Unified Cross-Modal Embedding Alignment for Face-Voice Association

arXiv: https://arxiv.org/abs/2512.06757 · HTML: https://mlsanity.com/paper/2512.06757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [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)
- [A Multi-View Approach To Audio-Visual Speaker Verification](https://mlsanity.com/paper/2102.06291/) (2021)
- [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)
- [PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Association](https://mlsanity.com/paper/2505.17002/) (2025)
- [Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan](https://mlsanity.com/paper/2508.04592/) (2025)

## Built on by (most cited first, 1 of 1)

- [Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge](https://mlsanity.com/paper/2512.20376/) (2025)

## Abstract (arXiv)

This paper introduces our solution, XM-ALIGN (Unified Cross-Modal Embedding Alignment Framework), proposed for the FAME challenge at ICASSP 2026. Our framework combines explicit and implicit alignment mechanisms, significantly improving cross-modal verification performance in both "heard" and "unheard" languages. By extracting feature embeddings from both face and voice encoders and jointly optimizing them using a shared classifier, we employ mean squared error (MSE) as the embedding alignment loss to ensure tight alignment between modalities. Additionally, data augmentation strategies are applied during model training to enhance generalization. Experimental results show that our approach demonstrates superior performance on the MAV-Celeb dataset. The code will be released at https://github.com/PunkMale/XM-ALIGN.