DescribePro: Collaborative Audio Description with Human-AI Interaction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913971617923072 |
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| author | Cheema, Maryam Elahimanesh, Sina Martin, Samuel Fazli, Pooyan Seifi, Hasti |
| author_facet | Cheema, Maryam Elahimanesh, Sina Martin, Samuel Fazli, Pooyan Seifi, Hasti |
| contents | Audio description (AD) makes video content accessible to millions of blind and low vision (BLV) users. However, creating high-quality AD involves a trade-off between the precision of human-crafted descriptions and the efficiency of AI-generated ones. To address this, we present DescribePro a collaborative AD authoring system that enables describers to iteratively refine AI-generated descriptions through multimodal large language model prompting and manual editing. DescribePro also supports community collaboration by allowing users to fork and edit existing ADs, enabling the exploration of different narrative styles. We evaluate DescribePro with 18 describers (9 professionals and 9 novices) using quantitative and qualitative methods. Results show that AI support reduces repetitive work while helping professionals preserve their stylistic choices and easing the cognitive load for novices. Collaborative tags and variations show potential for providing customizations, version control, and training new describers. These findings highlight the potential of collaborative, AI-assisted tools to enhance and scale AD authorship. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01092 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DescribePro: Collaborative Audio Description with Human-AI Interaction Cheema, Maryam Elahimanesh, Sina Martin, Samuel Fazli, Pooyan Seifi, Hasti Human-Computer Interaction Audio description (AD) makes video content accessible to millions of blind and low vision (BLV) users. However, creating high-quality AD involves a trade-off between the precision of human-crafted descriptions and the efficiency of AI-generated ones. To address this, we present DescribePro a collaborative AD authoring system that enables describers to iteratively refine AI-generated descriptions through multimodal large language model prompting and manual editing. DescribePro also supports community collaboration by allowing users to fork and edit existing ADs, enabling the exploration of different narrative styles. We evaluate DescribePro with 18 describers (9 professionals and 9 novices) using quantitative and qualitative methods. Results show that AI support reduces repetitive work while helping professionals preserve their stylistic choices and easing the cognitive load for novices. Collaborative tags and variations show potential for providing customizations, version control, and training new describers. These findings highlight the potential of collaborative, AI-assisted tools to enhance and scale AD authorship. |
| title | DescribePro: Collaborative Audio Description with Human-AI Interaction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.01092 |