Sound Sensing and Feedback Techniques for Deaf and Hard of Hearing People
| dc.contributor.advisor | Froehlich, Jon | |
| dc.contributor.advisor | Findlater, Leah | |
| dc.contributor.author | Jain, Dhruv | |
| dc.date.accessioned | 2022-09-23T20:44:27Z | |
| dc.date.available | 2022-09-23T20:44:27Z | |
| dc.date.issued | 2022-09-23 | |
| dc.date.submitted | 2022 | |
| dc.description | Thesis (Ph.D.)--University of Washington, 2022 | |
| dc.description.abstract | Sound awareness can have wide ranging impact for people who are deaf or hard of hearing (DHH) from being informed of safety critical sounds such as fire alarms and sirens to more mundane but useful cues such as microwave beeps or door knocks. The current solutions used by DHH people—such as flashing doorbells or vibratory alarm clocks—only substitute for some specific sounds, but do not provide a general awareness about environmental sounds. In this dissertation, I report on several iterative studies aimed at: understanding sound awareness needs and preferences of DHH people, designing systems to enhance sound and speech awareness in multiple contexts, and evaluating these systems in both controlled environments and in the field. I close with outlining the future grand challenges in the emerging field of sound accessibility. | |
| dc.embargo.terms | Open Access | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.other | Jain_washington_0250E_24833.pdf | |
| dc.identifier.uri | http://hdl.handle.net/1773/49312 | |
| dc.language.iso | en_US | |
| dc.rights | CC BY | |
| dc.subject | Accessibility | |
| dc.subject | Applied Machine Learning | |
| dc.subject | Deaf and Hard of Hearing | |
| dc.subject | Human-Computer Interaction | |
| dc.subject | Sound Accessibility | |
| dc.subject | Computer science | |
| dc.subject | Computer engineering | |
| dc.subject.other | Computer science and engineering | |
| dc.title | Sound Sensing and Feedback Techniques for Deaf and Hard of Hearing People | |
| dc.type | Thesis |
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