# Rescribe: Authoring and Automatically Editing Audio Descriptions

arXiv: https://arxiv.org/abs/2010.03667 · HTML: https://mlsanity.com/paper/2010.03667/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing](https://mlsanity.com/paper/2402.10294/) (2024)
- [WorldScribe: Towards Context-Aware Live Visual Descriptions](https://mlsanity.com/paper/2408.06627/) (2024)
- [Contextual AD Narration with Interleaved Multimodal Sequence](https://mlsanity.com/paper/2403.12922/) (2024)
- [DistinctAD: Distinctive Audio Description Generation in Contexts](https://mlsanity.com/paper/2411.18180/) (2024)
- [Vid2Coach: Transforming How-To Videos into Task Assistants](https://mlsanity.com/paper/2506.00717/) (2025)
- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [VideoA11y: Method and Dataset for Accessible Video Description](https://mlsanity.com/paper/2502.20480/) (2025)
- [What You See is What You Ask: Evaluating Audio Descriptions](https://mlsanity.com/paper/2510.00808/) (2025)
- [Lotus: Creating Short Videos From Long Videos With Abstractive and Extractive Summarization](https://mlsanity.com/paper/2502.07096/) (2025)
- [Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions](https://mlsanity.com/paper/2507.15692/) (2025)
- [A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilation](https://mlsanity.com/paper/2604.10456/) (2026)
- [Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies](https://mlsanity.com/paper/2410.08860/) (2024)
- [Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search](https://mlsanity.com/paper/2502.05924/) (2025)
- [NarrationBot and InfoBot: A Hybrid System for Automated Video Description](https://mlsanity.com/paper/2111.03994/) (2021)
- [Toward Scalable Audio Description Quality Control: A Workflow for Evaluating Human and VLM Raters](https://mlsanity.com/paper/2602.01390/) (2026)
- [From Visual Cues to Spoken Narration: Rethinking Audio Description](https://mlsanity.com/paper/2609.01725/) (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)
- [READ More than What You See: Reinforcement Learning for Accurate and Coherent Audio Description Generations](https://mlsanity.com/paper/2606.22766/) (2026)
- [What, When, and How: Audio Description as Constrained Global Optimization](https://mlsanity.com/paper/2609.30121/) (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 make videos accessible to those who cannot see them by describing visual content in audio. Producing audio descriptions is challenging due to the synchronous nature of the audio description that must fit into gaps of other video content. An experienced audio description author will produce content that fits narration necessary to understand, enjoy, or experience the video content into the time available. This can be especially tricky for novices to do well. In this paper, we introduce a tool, Rescribe, that helps authors create and refine their audio descriptions. Using Rescribe, authors first create a draft of all the content they would like to include in the audio description. Rescribe then uses a dynamic programming approach to optimize between the length of the audio description, available automatic shortening approaches, and source track lengthening approaches. Authors can iteratively visualize and refine the audio descriptions produced by Rescribe, working in concert with the tool. We evaluate the effectiveness of Rescribe through interviews with blind and visually impaired audio description users who preferred Rescribe-edited descriptions to extended descriptions. In addition, we invite novice users to create audio descriptions with Rescribe and another tool, finding that users produce audio descriptions with fewer placement errors using Rescribe.