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Recent advances have enabled automatic sound recognition systems for deaf andhard of hearing (DHH) users on mobile devices. However, these tools usepre-trained, generic sound recognition models, which do not meet the diverseneeds of DHH users. We introduce ProtoSound, an interactive system forcustomizing sound recognition models by recording a few examples, therebyenabling personalized and fine-grained categories. ProtoSound is motivated byprior work examining sound awareness needs of DHH people and by a survey weconducted with 472 DHH participants. To evaluate ProtoSound, we characterizedperformance on two real-world sound datasets, showing significant improvementover state-of-the-art (e.g., +9.7% accuracy on the first dataset). We thendeployed ProtoSound's end-user training and real-time recognition through amobile application and recruited 19 hearing participants who listened to thereal-world sounds and rated the accuracy across 56 locations (e.g., homes,restaurants, parks). Results show that ProtoSound personalized the modelon-device in real-time and accurately learned sounds across diverse acousticcontexts. We close by discussing open challenges in personalizable soundrecognition, including the need for better recording interfaces and algorithmicimprovements.

ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users