CapTune: Adapting Non-Speech Captions With Anchored Generative Models
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909755669217280 |
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| author | Huang, Jeremy Zhengqi Pataca, Caluã de Lacerda Wu, Liang-Yuan Jain, Dhruv |
| author_facet | Huang, Jeremy Zhengqi Pataca, Caluã de Lacerda Wu, Liang-Yuan Jain, Dhruv |
| contents | Non-speech captions are essential to the video experience of deaf and hard of hearing (DHH) viewers, yet conventional approaches often overlook the diversity of their preferences. We present CapTune, a system that enables customization of non-speech captions based on DHH viewers' needs while preserving creator intent. CapTune allows caption authors to define safe transformation spaces using concrete examples and empowers viewers to personalize captions across four dimensions: level of detail, expressiveness, sound representation method, and genre alignment. Evaluations with seven caption creators and twelve DHH participants showed that CapTune supported creators' creative control while enhancing viewers' emotional engagement with content. Our findings also reveal trade-offs between information richness and cognitive load, tensions between interpretive and descriptive representations of sound, and the context-dependent nature of caption preferences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19971 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CapTune: Adapting Non-Speech Captions With Anchored Generative Models Huang, Jeremy Zhengqi Pataca, Caluã de Lacerda Wu, Liang-Yuan Jain, Dhruv Human-Computer Interaction cs.HC, cs.AI Non-speech captions are essential to the video experience of deaf and hard of hearing (DHH) viewers, yet conventional approaches often overlook the diversity of their preferences. We present CapTune, a system that enables customization of non-speech captions based on DHH viewers' needs while preserving creator intent. CapTune allows caption authors to define safe transformation spaces using concrete examples and empowers viewers to personalize captions across four dimensions: level of detail, expressiveness, sound representation method, and genre alignment. Evaluations with seven caption creators and twelve DHH participants showed that CapTune supported creators' creative control while enhancing viewers' emotional engagement with content. Our findings also reveal trade-offs between information richness and cognitive load, tensions between interpretive and descriptive representations of sound, and the context-dependent nature of caption preferences. |
| title | CapTune: Adapting Non-Speech Captions With Anchored Generative Models |
| topic | Human-Computer Interaction cs.HC, cs.AI |
| url | https://arxiv.org/abs/2508.19971 |