Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing
Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing
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DOI：
10.1145/3463501
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发表时间：
2021-06
影响因子：
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通讯作者：
Steven M. Goodman;Ping Liu;Emma J. McDonnell;Jon E. Froehlich;Steven M. Goodman;Ping Liu;D. Jain;Emma J. McDonnell;Jon E. Froehlich
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作者：
Steven M. Goodman;Ping Liu;Emma J. McDonnell;Jon E. Froehlich;Steven M. Goodman;Ping Liu;D. Jain;Emma J. McDonnell;Jon E. Froehlich
自动化的声音识别工具可以是d/聋人和听力困难（DHH）人的典型通信和环境意识策略的有用补充。然而，预先训练的声音识别模型可能无法满足个体DHH用户的不同需求。虽然以人为中心的机器学习方法可以使非专家用户能够构建自己的自动化系统，但增强人类感官能力的最终用户ML解决方案为感官残疾的用户带来了独特的挑战：DHH用户如何能够自己难以听到声音，有效地记录样本来训练ML系统识别该声音？为了更好地了解DHH用户如何驱动他们自己的辅助声音识别工具的个性化，我们对14名DHH参与者进行了一项由三部分组成的研究：（1）个性化声音识别器的初步访谈和演示，（2）为期一周的现场录音研究，以及（3）后续访谈和构思会议。我们的研究结果突出了一个积极的主观体验时，记录和解释训练数据在现场，但我们发现了几个关键的陷阱，独特的DHH用户-如抑制判断的代表性样本，由于有限的听力经验。我们分享这些结果的录音界面和人的循环系统，可以支持DHH用户建立自己的个人需要的声音识别器的设计的影响。
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.