Supporting Accessible Data Visualization Through Audio Data Narratives

Alexa F. Siu, Gene S-H Kim, Sile O’Modhrain, and Sean Follmer
CHI: ACM Conference on Human Factors in Computing Systems, 2022
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 de- scriptions and sonifcation (the mapping of data to non-speech sounds). We conduct two co-design workshops with screen reader users to defne 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 signifcantly more insights from the data. Users describe data narratives help them better extract and comprehend the information in both the sonification and description.