Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition (ISLR) dataset, collected with consent and containing 83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of environments. We propose that this dataset be used for sign language dictionary retrieval for American Sign Language (ASL), where a user demonstrates a sign to their webcam to retrieve matching signs from a dictionary. We show that training supervised machine learning classifiers with our dataset advances the state-of-the-art on metrics relevant for dictionary retrieval, achieving 63% accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who are not present in the training or validation sets. @article{desai2023asl, title={ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition}, author={Desai, Aashaka and Berger, Lauren and Minakov, Fyodor O and Milan, Vanessa and Singh, Chinmay and Pumphrey, Kriston and Ladner, Richard E and Daum{\'e} III, Hal and Lu, Alex X and Caselli, Naomi and Bragg, Danielle}, journal={arXiv preprint arXiv:2304.05934}, year={2023} }

2M-Flores

As part of the 2M-Belebele project, we have produced video recodings of ASL signing for all the dev and devtest sentences in the original flores200 dataset. To obtain ASL sign recordings, we provide translators of ASL and native signers with the English text version of the sentences to be recorded. The interpreters are then asked to translate these sentences into ASL, create glosses for all sentences, and record their interpretations into ASL one sentence at a time. The… See the full description on the dataset page: https://huggingface.co/datasets/facebook/2M-Flores-ASL.