# Unsupervised Sign Language Phoneme Clustering using HamNoSys Notation

arXiv: https://arxiv.org/abs/2205.10560 · HTML: https://mlsanity.com/paper/2205.10560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [Counting Out Time: Class Agnostic Video Repetition Counting in the Wild](https://mlsanity.com/paper/2006.15418/) (2020)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [Towards Large-Scale Data Mining for Data-Driven Analysis of Sign Languages](https://mlsanity.com/paper/2006.02120/) (2020)

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

- [On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study](https://mlsanity.com/paper/2302.10768/) (2023)

## Abstract (arXiv)

Traditionally, sign language resources have been collected in controlled settings for specific tasks involving supervised sign classification or linguistic studies accompanied by specific annotation type. To date, very few who explored signing videos found online on social media platforms as well as the use of unsupervised methods applied to such resources. Due to the fact that the field is striving to achieve acceptable model performance on the data that differs from that seen during training calls for more diversity in sign language data, stepping away from the data obtained in controlled laboratory settings. Moreover, since the sign language data collection and annotation carries large overheads, it is desirable to accelerate the annotation process. Considering the aforementioned tendencies, this paper takes the side of harvesting online data in a pursuit for automatically generating and annotating sign language corpora through phoneme clustering.