mlsanityData to 7 Oct 2026

Rescribe: Authoring and Automatically Editing Audio Descriptions

arXiv 2010.03667 ↗First posted 7 Oct 2020cs.HCcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 5; 2025: 6; 2026: 7.

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  1. 2024LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editingcited by 47
  2. 2024WorldScribe: Towards Context-Aware Live Visual Descriptionscited by 26
  3. 2024Contextual AD Narration with Interleaved Multimodal Sequencecited by 14
  4. 2025Vid2Coach: Transforming How-To Videos into Task Assistantscited by 12
  5. 2023Semantic Hearing: Programming Acoustic Scenes with Binaural Hearablescited by 11
  6. 2024DistinctAD: Distinctive Audio Description Generation in Contextscited by 11
  7. 2025VideoA11y: Method and Dataset for Accessible Video Descriptioncited by 8
  8. 2025What You See is What You Ask: Evaluating Audio Descriptionscited by 7
  9. 2025Lotus: Creating Short Videos From Long Videos With Abstractive and Extractive Summarizationcited by 6
  10. 2025Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptionscited by 6
  11. 2026A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilationcited by 6
  12. 2024Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologiescited by 4
8 more
  1. 2025Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Searchcited by 2
  2. 2021NarrationBot and InfoBot: A Hybrid System for Automated Video Descriptioncited by 1
  3. 2026From Visual Cues to Spoken Narration: Rethinking Audio Descriptioncited by 1
  4. 2026Toward Scalable Audio Description Quality Control: A Workflow for Evaluating Human and VLM Raterscited by 0
  5. 2026Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Playercited by 0
  6. 2026Making AI Drafts Count: A Quality Threshold in Audio Description Workflowscited by 0
  7. 2026READ More than What You See: Reinforcement Learning for Accurate and Coherent Audio Description Generationscited by 0
  8. 2026What, When, and How: Audio Description as Constrained Global Optimizationcited by 0