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The COVID-19 pandemic forced many people to convert their daily work lives to a "virtual" format where everyone connected remotely from their home. In this new, virtual environment, accessibility barriers changed, in some respects for the better (e.g., more flexibility) and in other aspects, for the worse (e.g., problems including American Sign Language interpreters over video calls). Microsoft Research held its first cohort of all virtual interns in 2020. We the authors, full time and intern members and affiliates of the Ability Team, a research team focused on accessibility, reflect on our virtual work experiences as a team consisting of members with a variety of abilities, positions, and seniority during the summer intern season. Through our autoethnographic method, we provide a nuanced view into the experiences of a mixed-ability, virtual team, and how the virtual setting affected the team's accessibility. We then reflect on these experiences, noting the successful strategies we used to promote access and the areas in which we could have further improved access. Finally, we present guidelines for future virtual mixed-ability teams looking to improve access. [ABSTRACT FROM AUTHOR]

Many Deaf and Hard-of-Hearing (DHH) individuals rely on sign language interpreting to communicate with hearing peers. If on-site interpreting is not available, DHH individuals may use remote interpreting over a smartphone video-call. However, this solution requires the DHH individual to give up either 1) the use of one signing hand by holding the smartphone or 2) their ability to multitask and move around by propping the smartphone up in a fixed location. We explore this problem within the context of the workplace, and present a prototype hands-free device using augmented reality glasses with a hat-mounted fisheye camera and mic/speaker. To explore the validity of our design, we conducted 1) a video interpretability experiment, and 2) a user study with 18 participants (9 DHH, 9 hearing) in a workplace environment. Our results suggest that a hands-free device can support accurate interpretation while enhancing personal interactions. [ABSTRACT FROM AUTHOR]

Sounds provide informative signals about the world around us. In situations where non-auditory cues are inaccessible, it can be useful for deaf and hard-of-hearing people to be notified about sounds. Through a survey, we explored which sounds are of interest to deaf and hard-of-hearing people, and which means of notification are appropriate. Motivated by these findings, we designed a mobile phone app that alerts deaf and hard-of-hearing people to sounds they care about. The app uses training examples of personally relevant sounds recorded by the user to learn a model of those sounds. It then screens the incoming audio stream from the phone's microphone for those sounds. When it detects a sound, it alerts the user by vibrating and providing a pop-up notification. To evaluate the interface design independent of sound detection errors, we ran a Wizard-of-Oz user study, and found that the app design successfully facilitated deaf and hard-of-hearing users recording training examples. We also explored the viability of a basic machine learning algorithm for sound detection. [ABSTRACT FROM AUTHOR]