# Exploring Strategies for Modeling Sign Language Phonology

arXiv: https://arxiv.org/abs/2310.00195 · HTML: https://mlsanity.com/paper/2310.00195/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages](https://mlsanity.com/paper/2110.05877/) (2021)
- [WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language](https://mlsanity.com/paper/2203.06096/) (2022)
- [Improving Sign Recognition with Phonology](https://mlsanity.com/paper/2302.05759/) (2023)

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

- [The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition](https://mlsanity.com/paper/2409.15284/) (2024)
- [The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge](https://mlsanity.com/paper/2411.03568/) (2024)
- [Massively Multilingual Text Translation For Low-Resource Languages](https://mlsanity.com/paper/2401.16582/) (2024)
- [Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach](https://mlsanity.com/paper/2509.18309/) (2025)

## Abstract (arXiv)

Like speech, signs are composed of discrete, recombinable features called phonemes. Prior work shows that models which can recognize phonemes are better at sign recognition, motivating deeper exploration into strategies for modeling sign language phonemes. In this work, we learn graph convolution networks to recognize the sixteen phoneme "types" found in ASL-LEX 2.0. Specifically, we explore how learning strategies like multi-task and curriculum learning can leverage mutually useful information between phoneme types to facilitate better modeling of sign language phonemes. Results on the Sem-Lex Benchmark show that curriculum learning yields an average accuracy of 87% across all phoneme types, outperforming fine-tuning and multi-task strategies for most phoneme types.