VoiceAssist

Seetharaman, Prem; Mysore, Gautham J.; Pardo, Bryan; Smaragdis, Paris; Gomes, Celso

doi:10.1145/3290605.3300539

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Indeed, in Nakao et al's [58] study of DHH users' technical understanding of ML, workshop participants struggled to choose acceptable sound samples for training due to a lack of non-auditory feedback. For work on sampling feedback, VoiceAssist [65] provides real-time visual feedback to help inexperienced users reduce reverberation and background noise in voice recordings, and a user study showed third party listeners preferred recordings made using VoiceAssist compared to those without. To our knowledge, however, prior work has yet to explore sound sampling feedback for sound recognition with any population-a gap which we begin to address with our work.…”

Section: Human-centered Machine Learningmentioning

confidence: 99%

Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing

Goodman

Liu

Jain

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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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.

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Section: Human-centered Machine Learningmentioning

confidence: 99%