# Describe Now: User-Driven Audio Description for Blind and Low Vision Individuals

arXiv: https://arxiv.org/abs/2411.11835 · HTML: https://mlsanity.com/paper/2411.11835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [Large Language Models for Web Accessibility: A Systematic Literature Review](https://mlsanity.com/paper/2605.13873/) (2026)
- [Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player](https://mlsanity.com/paper/2602.04104/) (2026)
- [How Do Novice Describers Edit AI Drafts? The Role of Draft Quality in Audio Description Authoring](https://mlsanity.com/paper/2605.05348/) (2026)
- [Small yet Assistive: Spatially-Aware Post-Training for Low Vision](https://mlsanity.com/paper/2609.28757/) (2026)
- [AD4EDU: Benchmarking University Lecture Audio Description across Accessibility Standards, Metrics, and Learner Preferences](https://mlsanity.com/paper/2610.17042/) (2026)

## Abstract (arXiv)

Audio descriptions (AD) make videos accessible for blind and low vision (BLV) users by describing visual elements that cannot be understood from the main audio track. AD created by professionals or novice describers is time-consuming and offers little customization or control to BLV viewers on description length and content and when they receive it. To address this gap, we explore user-driven AI-generated descriptions, enabling BLV viewers to control both the timing and level of detail of the descriptions they receive. In a study, 20 BLV participants activated audio descriptions for seven different video genres with two levels of detail: concise and detailed. Our findings reveal differences in the preferred frequency and level of detail of ADs for different videos, participants' sense of control with this style of AD delivery, and its limitations. We discuss the implications of these findings for the development of future AD tools for BLV users.