PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones

Published: 26 Sept 2023, Last Modified: 16 Jan 2024NeurIPS 2023 Datasets and Benchmarks PosterEveryoneRevisionsBibTeX
Keywords: American Sign Language, gesture, dataset
TL;DR: Introduces PopSign ASL v1.0: a public isolated American Sign Language dataset of ~200,000 examples of 250 signs
Abstract: PopSign is a smartphone-based bubble-shooter game that helps hearing parents of deaf infants learn sign language. To help parents practice their ability to sign, PopSign is integrating sign language recognition as part of its gameplay. For training the recognizer, we introduce the PopSign ASL v1.0 dataset that collects examples of 250 isolated American Sign Language (ASL) signs using Pixel 4A smartphone selfie cameras in a variety of environments. It is the largest publicly available, isolated sign dataset by number of examples and is the first dataset to focus on one-handed, smartphone signs. We collected over 210,000 examples at 1944x2592 resolution made by 47 consenting Deaf adult signers for whom American Sign Language is their primary language. We manually reviewed 217,866 of these examples, of which 175,022 (approximately 700 per sign) were the sign intended for the educational game. 39,304 examples were recognizable as a sign but were not the desired variant or were a different sign. We provide a training set of 31 signers, a validation set of eight signers, and a test set of eight signers. A baseline LSTM model for the 250-sign vocabulary achieves 82.1% accuracy (81.9% class-weighted F1 score) on the validation set and 84.2% (83.9% class-weighted F1 score) on the test set. Gameplay suggests that accuracy will be sufficient for creating educational games involving sign language recognition.
Supplementary Material: pdf
Submission Number: 952