# Emotion Recognition in Signers

arXiv: https://arxiv.org/abs/2512.15376 · HTML: https://mlsanity.com/paper/2512.15376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition](https://mlsanity.com/paper/2311.11009/) (2023)
- [TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2401.12987/) (2024)
- [ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP)](https://mlsanity.com/paper/2201.07899/) (2022)
- [American Sign Language Handshapes Reflect Pressures for Communicative Efficiency](https://mlsanity.com/paper/2406.04024/) (2024)
- [Sign Language Recognition Based On Facial Expression and Hand Skeleton](https://mlsanity.com/paper/2407.02241/) (2024)

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

- [Emotion Recognition in Sign Language Conversation](https://mlsanity.com/paper/2605.23328/) (2026)

## Abstract (arXiv)

Recognition of signers' emotions suffers from one theoretical challenge and one practical challenge, namely, the overlap between grammatical and affective facial expressions and the scarcity of data for model training. This paper addresses these two challenges in a cross-lingual setting using our eJSL dataset, a new benchmark dataset for emotion recognition in Japanese Sign Language signers, and BOBSL, a large British Sign Language dataset with subtitles. In eJSL, two signers expressed 78 distinct utterances with each of seven different emotional states, resulting in 1,092 video clips. We empirically demonstrate that 1) textual emotion recognition in spoken language mitigates data scarcity in sign language, 2) temporal segment selection has a significant impact, and 3) incorporating hand motion enhances emotion recognition in signers. Finally we establish a stronger baseline than spoken language LLMs.