  Recent advances have enabled automatic sound recognition systems for deaf and
hard of hearing (DHH) users on mobile devices. However, these tools use
pre-trained, generic sound recognition models, which do not meet the diverse
needs of DHH users. We introduce ProtoSound, an interactive system for
customizing sound recognition models by recording a few examples, thereby
enabling personalized and fine-grained categories. ProtoSound is motivated by
prior work examining sound awareness needs of DHH people and by a survey we
conducted with 472 DHH participants. To evaluate ProtoSound, we characterized
performance on two real-world sound datasets, showing significant improvement
over state-of-the-art (e.g., +9.7% accuracy on the first dataset). We then
deployed ProtoSound's end-user training and real-time recognition through a
mobile application and recruited 19 hearing participants who listened to the
real-world sounds and rated the accuracy across 56 locations (e.g., homes,
restaurants, parks). Results show that ProtoSound personalized the model
on-device in real-time and accurately learned sounds across diverse acoustic
contexts. We close by discussing open challenges in personalizable sound
recognition, including the need for better recording interfaces and algorithmic
improvements.
