ASL champ!: a virtual reality game with deep-learning driven sign recognition
Md. Shahinur Alam, Jason Lamberton, Jianye Wang, Carly Leannah, Sarah Miller, Joseph Palagano, Myles de Bastion, Heather L. Smith, Melissa Malzkuhn, Lorna C. Quandt
Abstract
We developed an American Sign Language (ASL) learning platform in a Virtual Reality (VR) environment to facilitate immersive interaction and real-time feedback for ASL learners. We describe the first game to use an interactive teaching style in which users learn from a fluent signing avatar and the first implementation of ASL sign recognition using deep learning within the VR environment. Advanced motion-capture technology powers an expressive ASL teaching avatar within an immersive three-dimensional environment. The teacher demonstrates an ASL sign for an object, prompting the user to copy the sign. Upon the user’s signing, a third-party plugin executes the sign recognition process alongside a deep learning model. Depending on the accuracy of a user’s sign production, the avatar repeats the sign or introduces a new one. We gathered a 3D VR ASL dataset from fifteen diverse participants to power the sign recognition model. The proposed deep learning model’s training, validation, and test accuracy are 90.12%, 89.37%, and 86.66%, respectively. The functional prototype can teach sign language vocabulary and be successfully adapted as an interactive ASL learning platform in VR.