Peer Reviewed

Current sound recognition systems for deaf and hard of hearing (DHH) people identify sound sources or discrete events. However, these systems do not distinguish similar sounding events (e.g., a patient monitor beep vs. a microwave beep). In this paper, we introduce HACS, a novel futuristic approach to designing human-AI sound awareness systems. HACS assigns AI models to identify sounds based on their characteristics (e.g., a beep) and prompts DHH users to use this information and their contextual knowledge (e.g., “I am in a kitchen”) to recognize sound events (e.g., a microwave). As a first step for implementing HACS, we articulated a sound taxonomy that classifies sounds based on sound characteristics using insights from a multi-phased research process with people of mixed hearing abilities. We then performed a qualitative (with 9 DHH people) and a quantitative (with a sound recognition model) evaluation. Findings demonstrate the initial promise of HACS for designing accurate and reliable human-AI systems.

While recent advances have enabled mobile sound recognition tools for deaf and hard of hearing (DHH) people, these tools have only been studied in the lab or through short, controlled experiments. To assess the real-world feasibility and guide the future designs of mobile sound awareness systems, we conducted a three-week field study of SoundWatch, a smartwatch-based sound recognition app, with 10 DHH participants. Our findings suggest the app's utility in increasing environmental awareness and facilitating everyday tasks for DHH users. However, several challenges, such as background noises, variability of real-world sounds, and confusion among similar sounding sounds, indicated that mobile sound recognition solutions are “not there yet” for adoption and use in daily life. We close by presenting HCI design opportunities to improve model reliability by increasing contextual awareness, supporting end-user customization, and fostering the collective improvement of sound recognition models.