Collaborative writing tools have been used widely in professional and academic organizations for many years. Yet, there has not been much work to improve screen reader access in mainstream collaborative writing tools. This severely affects the way people with vision impairments collaborate in ability-diverse teams. As a step toward addressing this issue, the present article aims at improving screen reader representation of collaborative features such as comments and track changes (i.e., suggested edits). Building on our formative interviews with 20 academics and professionals with vision impairments, we developed auditory representations that indicate comments and edits using non-speech audio (e.g., earcons, tone overlay), multiple text-to-speech voices, and contextual presentation techniques. We then performed a systematic evaluation study with 48 screen reader users that indicated that non-speech audio, changing voices, and contextual presentation can potentially improve writers’ collaboration awareness. We discuss implications of these results for the design of accessible collaborative systems.
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Supporting Accessible Data Visualization Through Audio Data Narratives
Online data visualizations play an important role in informing public opinion but are often inaccessible to screen reader users. To address the need for accessible data representations on the web that provide direct, multimodal, and up-to-date access to the data, we investigate audio data narratives –which combine textual descriptions and sonification (the mapping of data to non-speech sounds). We conduct two co-design workshops with screen reader users to define design principles that guide the structure, content, and duration of a data narrative. Based on these principles and relevant auditory processing characteristics, we propose a dynamic programming approach to automatically generate an audio data narrative from a given dataset. We evaluate our approach with 16 screen reader users. Findings show with audio narratives, users gain significantly more insights from the data. Users describe data narratives help them better extract and comprehend the information in both the sonification and description.
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- Award ID(s):
- 2016789
- NSF-PAR ID:
- 10379155
- Date Published:
- Journal Name:
- CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3491102.3517678
