We introduce a new problem domain for activity recognition

Head and neck cancer can significantly hamper speech production which often reduces speech intelligibility. A method of extracting spectral features is presented. The method uses a multi-resolution sinusoidal transform scheme, which enables better representation of spectral and harmonic characteristics. Regression methods were used to predict interval-scaled intelligibility scores of utterances in the NKI-CCRT speech corpus. The inclusion of these features lowered the mean squared estimation error from 0.43 to 0.39 on a scale from 1 to 7, with a p-value less than 0.001. For binary intelligibility classification, their inclusion resulted in an improvement by 5.0 percentage points when tested on a disjoint set.

There has been a growing interest in Computer-Supported Cooperative Work and Human-Computer Interaction to understand the experiences of programmers in the workplace. However, the large majority of these studies has focused on sighted programmers and, as a result, the experiences of programmers with visual impairments in professional contexts remain understudied. We address this gap by reporting on findings from semi-structured interviews with 22 programmers with visual impairments. We found that programmers with visual impairments interact with a complex ecosystem of tools and a significant part of their job entails performing work to overcome the accessibility challenges inherent in this ecosystem. Furthermore, we find that the visual nature of various programming activities impedes collaboration, which necessitates the co-creation of new work practices through a series of sociotechnical interactions. These sociotechnical interactions often require invisible work and articulation work on the part of the programmers with visual impairments.

Data visualization has become an increasingly important means of effective data communication and has played a vital role in broadcasting the progression of COVID-19. Accessible data representations, on the other hand, have lagged behind, leaving areas of information out of reach for many blind and visually impaired (BVI) users. In this work, we sought to understand (1) the accessibility of current implementations of visualizations on the web; (2) BVI users’ preferences and current experiences when accessing data-driven media; (3) how accessible data representations on the web address these users’ access needs and help them navigate, interpret, and gain insights from the data; and (4) the practical challenges that limit BVI users’ access and use of data representations. To answer these questions, we conducted a mixed-methods study consisting of an accessibility audit of 87 data visualizations on the web to identify accessibility issues, an online survey of 127 screen reader users to understand lived experiences and preferences, and a remote contextual inquiry with 12 of the survey respondents to observe how they navigate, interpret and gain insights from accessible data representations. Our observations during this critical period of time provide an understanding of the widespread accessibility issues encountered across online data visualizations, the impact that data accessibility inequities have on the BVI community, the ways screen reader users sought access to data-driven information and made use of online visualizations to form insights, and the pressing need to make larger strides towards improving data literacy, building confidence, and enriching methods of access. Based on our findings, we provide recommendations for researchers and practitioners to broaden data accessibility on the web.

No abstract