Supporting Accessible Data Visualization Through Audio Data Narratives
Alexa F Siu, Gene S-H Kim, Sile O'Modhrain, Sean Follmer
CHI'22: ACM Conference on Human Factors in Computing Systems
Session: Accessibility and Data Visualization
Abstract
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 interleave 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 for automatically generating an audio data narrative from a given dataset. We evaluate our proposed approach with 16 screen reader users. We find that users gain significantly more data insights when consuming the information in narrative form compared to the standard sonification approach.
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Pre-recorded presentations of CHI 2022
