To help novice signers learn American Sign Language, we develop PopSignAI, a proof-of-concept smartphone-based bubble-shooter game that facilitates real-time interaction through isolated sign language recognition. In a 20-person user study, we demonstrate that encouraging novice signers to practice generating sign in PopSignAI is more efficient for teaching ASL skills than a version of PopSign focused on receptive signing ability. We use over 200,000 examples of 250 signs from 47 signers to train and test a user-independent LSTM recognizer that achieves 82.9% accuracy on an independent test set. For the purposes of the game, the recognizer averages 99.6% accuracy with a 7ms inference time using a 2.5MB model. Ablation studies suggest that as few as eight signers are need for training in order for adequate recognition accuracy for PopSignAI’s gameplay. To encourage future sign language recognition games, we release the PopSignAI recognition pipeline and software. We identify hearing parents of deaf children as important potential users of sign games and conduct interviews with eight of these parents, investigating their motivation and challenges in learning sign.

PopSign is a smartphone-based bubble-shooter game that helps hearing parentsof deaf infants learn sign language. To help parents practice their ability to sign,PopSign is integrating sign language recognition as part of its gameplay. Fortraining the recognizer, we introduce the PopSign ASL v1.0 dataset that collectsexamples of 250 isolated American Sign Language (ASL) signs using Pixel 4Asmartphone selfie cameras in a variety of environments. It is the largest publiclyavailable, isolated sign dataset by number of examples and is the first dataset tofocus on one-handed, smartphone signs. We collected over 210,000 examplesat 1944x2592 resolution made by 47 consenting Deaf adult signers for whomAmerican Sign Language is their primary language. We manually reviewed 217,866of these examples, of which 175,023 (approximately 700 per sign) were the signintended for the educational game. 39,304 examples were recognizable as a signbut were not the desired variant or were a different sign. We provide a training setof 31 signers, a validation set of eight signers, and a test set of eight signers. Abaseline 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-weightedF1 score) on the test set. Gameplay suggests that accuracy will be sufficient forcreating educational games involving sign language recognition.