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

Authors:

Khoa Huynh Anh Nguyen,

Steven M. Goodman,

Rachel Grossman-Kahn,

Aditya Kusupati,

Leah Findlater, and

Jon E. FroehlichAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

Article No.: 305, Pages 1 - 16

https://doi.org/10.1145/3491102.3502020

Published: 29 April 2022 Publication History

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Abstract

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.

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Index Terms

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

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

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https://dl.acm.org/doi/10.1145/3643834.3661556
Dotch EJohnson JBlack RHayes G(2023)Understanding Noise Sensitivity through Interactions in Two Online Autism ForumsProceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3597638.3608413(1-12)Online publication date: 22-Oct-2023
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Li ZConnell SDannels WPeiris R(2022)SoundVizVR: Sound Indicators for Accessible Sounds in Virtual Reality for Deaf or Hard-of-Hearing UsersProceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3517428.3544817(1-13)Online publication date: 23-Oct-2022
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