Audio Description Customization
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Abstract (arXiv)
Blind and low-vision (BLV) people use audio descriptions (ADs) to access videos. However, current ADs are unalterable by end users, thus are incapable of supporting BLV individuals' potentially diverse needs and preferences. This research investigates if customizing AD could improve how BLV individuals consume videos. We conducted an interview study (Study 1) with fifteen BLV participants, which revealed desires for customizing properties like length, emphasis, speed, voice, format, tone, and language. At the same time, concerns like interruptions and increased interaction load due to customization emerged. To examine AD customization's effectiveness and tradeoffs, we designed CustomAD, a prototype that enables BLV users to customize AD content and presentation. An evaluation study (Study 2) with twelve BLV participants showed using CustomAD significantly enhanced BLV people's video understanding, immersion, and information navigation efficiency. Our work illustrates the importance of AD customization and offers a design that enhances video accessibility for BLV individuals.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Aug 2024.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Modelscited by 13
- 2025Vid2Coach: Transforming How-To Videos into Task Assistantscited by 12
- 2025VideoA11y: Method and Dataset for Accessible Video Descriptioncited by 9
- 2026Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Playercited by 0
- 2026Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player4 Feb 2026
- 2025Vid2Coach: Transforming How-To Videos into Task Assistants31 May 2025
- 2025VideoA11y: Method and Dataset for Accessible Video Description27 Feb 2025
- 2025Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models22 Feb 2025