CopyCat: Using Sign Language Recognition to Help Deaf Children Acquire Language Skills

Thad Starner
Prerna Ravi, Matthew So, Pranay Agrawal, Ishan Chadha, Ganesh Murugappan, Colby Duke, Gururaj Deshpande

Deaf children born to hearing parents lack continuous access to language, leading to weaker working memory compared to hearing children and deaf children born to Deaf parents. CopyCat is a game where children communicate with the computer via American Sign Language (ASL), and it has been shown to improve language skills and working memory. Previously, CopyCat depended on unscalable hardware such as custom gloves for sign verification, but modern 4K cameras and pose estimators present new opportunities. Before re-creating the CopyCat game for deaf children using off-the-shelf hardware, we evaluate whether current ASL recognition is sufficient. Using Hidden Markov Models (HMMs), user independent word accuracies were 90.6%, 90.5%, and 90.4% for AlphaPose, Kinect, and MediaPipe, respectively. Transformers, a state-of-the-art model in natural language processing, performed 17.0% worse on average. Given these results, we believe our current HMM-based recognizer can be successfully adapted to verify children's signing while playing CopyCat.

Thad Starner

The Contextual Computing Group (CCG) creates wearable and ubiquitous computing technologies using techniques from artificial intelligence (AI) and human-computer interaction (HCI). We focus on giving users superpowers through augmenting their senses, improving learning, and providing intelligent assistants in everyday life. Members' long-term projects have included creating wearable computers (Google Glass), teaching manual skills without attention (Passive Haptic Learning), improving hand sensation after traumatic injury (Passive Haptic Rehabilitation), educational technology for the Deaf community, and communicating with dogs and dolphins through computer interfaces (Animal Computer Interaction).