Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing
| Autor: | Dhruv Jain, Emma J. McDonnell, Steven N. Goodman, Ping Liu, Jon E. Froehlich, Leah Findlater |
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| Rok vydání: | 2021 |
| Předmět: |
geography
geography.geographical_feature_category Computer Networks and Communications Computer science 05 social sciences Judgement 020207 software engineering 02 engineering and technology Session (web analytics) Field (computer science) Personalization Human-Computer Interaction User driven Hardware and Architecture Human–computer interaction 0202 electrical engineering electronic engineering information engineering Key (cryptography) 0501 psychology and cognitive sciences Sound recognition 050107 human factors Sound (geography) |
| Zdroj: | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 5:1-23 |
| ISSN: | 2474-9567 |
| Popis: | Automated sound recognition tools can be a useful complement to d/Deaf and hard of hearing (DHH) people's typical communication and environmental awareness strategies. Pre-trained sound recognition models, however, may not meet the diverse needs of individual DHH users. While approaches from human-centered machine learning can enable non-expert users to build their own automated systems, end-user ML solutions that augment human sensory abilities present a unique challenge for users who have sensory disabilities: how can a DHH user, who has difficulty hearing a sound themselves, effectively record samples to train an ML system to recognize that sound? To better understand how DHH users can drive personalization of their own assistive sound recognition tools, we conducted a three-part study with 14 DHH participants: (1) an initial interview and demo of a personalizable sound recognizer, (2) a week-long field study of in situ recording, and (3) a follow-up interview and ideation session. Our results highlight a positive subjective experience when recording and interpreting training data in situ, but we uncover several key pitfalls unique to DHH users---such as inhibited judgement of representative samples due to limited audiological experience. We share implications of these results for the design of recording interfaces and human-the-the-loop systems that can support DHH users to build sound recognizers for their personal needs. |
| Databáze: | OpenAIRE |
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