High-quality closed captioning of both speech and non-speech elements (e.g., music, sound effects, manner of speaking, and speaker identification) is essential for the accessibility of video content, especially for d/Deaf and hard-of-hearing individuals. While many regions have regulations mandating captioning for television and movies, a regulatory gap remains for the vast amount of web-based video content, including the staggering 500+ hours uploaded to YouTube every minute. Advances in automatic speech recognition have bolstered the presence of captions on YouTube. However, the technology has notable limitations, including the omission of many non-speech elements, which are often crucial for understanding content narratives. This paper examines the contemporary and historical state of non-speech information (NSI) captioning on YouTube through the creation and exploratory analysis of a dataset of over 715k videos. We identify factors that influence NSI caption practices and suggest avenues for future research to enhance the accessibility of online video content.

With technological advancements, audio engineering has evolved from a domain exclusive to professionals to one open to amateurs. However, research is limited on the accessibility of audio engineering, particularly for deaf, Deaf, and hard of hearing (DHH) individuals. To bridge this gap, we interviewed eight deaf and hard of hearing (dHH) audio engineers in music to understand accessibility in audio engineering. We found that their hearing magnified challenges in audio engineering: insecurities in sound perception undermined their confidence, and the required extra “hearing work” added complexity. As workarounds, participants employed various technologies and techniques, relied on the support of hearing peers, and developed strategies for learning and growth. Through these practices, they navigate audio engineering while balancing confidence and limitations. For future directions, we recommend exploring technologies that reduce insecurities and “hearing work” to empower DHH audio engineers and working toward a DHH-community-driven approach to accessible audio engineering.

Creative sound activities, such as music playing and audio engineering, are said to have been democratized with the development of technology. Yet, the use of technology in creative sound activities by people who are deaf, Deaf, and hard of hearing (DHH) has been underexplored by the research community. To address this gap, we conducted an online survey with 50 DHH participants to understand their use of technology and barriers they face in their creative sound activities. We find DHH people use four types of technology — hearing devices, sound manipulation, sound visualization, and speech-to-text — for three purposes — to improve sound perception via auditory and visual means, to avoid hearing fatigue, and to better communicate with hearing people. We also find their barriers to technology: unknown availability, limited options, and limitations that technology can solve. We discuss opportunities for more inclusive design specific to DHH people’s creative sound activities, as well as facilitating access to information about technology.

Math graphs need to be accessible to People with Visual Impairments (PVI). While tactile graphics are a common way for PVI to access math graphs, their use becomes complicated in remote learning. To make math graphs more accessible in remote education, we focused on sonification, the use of non-speech sound. In this study, we designed techniques of sonification of math graphs to introduce the concept of discontinuity in calculus to PVI. First, we conducted a remote interview with six participants to understand their experiences with math education using graphs. Based on these findings, we developed a series of sonifications of math graphs that we remotely evaluated with three participants from our initial interviews. Our findings reveal that sonification can intuitively convey simple patterns and trends in math graphs with a little practice, be useful to introduce the discontinuities, and be more effective with descriptions of the sound and graphs.

This paper focuses on testing the current viability of using quantum computers for the processing of data-driven tasks fueled by emerging data science applications. We test the publicly available IBM quantum computers using Grover's algorithm, a well-known quantum search algorithm, to obtain a baseline for the general evaluations of these quantum devices and to investigate the impacts of various factors such as number of quantum bits (or qubits), qubit choice, and device choice. The main contributions of this paper include a new 4-qubit implementation of Grover's algorithm and test results showing the current capabilities of quantum computers. Our study indicates that quantum computers can currently only be used accurately for solving simple problems with very small amounts of data. There are also notable differences between different selections of the qubits in the implementation design and between different quantum devices that execute the algorithm.