简要介绍AI驱动的可听现实 (AuRa) 在开放的世界:潜力,应用和评估
Ömer Ates1, Garima Pandey1, Athanasios Gousiopoulos2,3
1School of Information, Media, and Design, SRH Hochschule Heidelberg, SRH University of Applied Science, Heidelberg, Germany.
Frontiers in artificial intelligence
|November 11, 2024
概括
人工智能 (AI) 和机器学习 (ML) 可以提高公共卫生的可访问性. 这项研究探讨了使用AI/ML用于可听现实 (AuRa) 来将视觉信息转换为视觉障碍者使用的音频.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能和机器学习越来越多地影响公共卫生,从药物发现到医疗保健运营.
- 现有的AI/ML应用程序可能无法完全解决残疾人面临的挑战,特别是在紧急情况下.
- 视力障碍对解释环境构成重大障碍.
研究的目的:
- 探索AI/ML在创造"可听现实" (AuRa) 的潜力.
- 为视觉障碍者提供视觉信息的实时,多语言音频翻译.
- 为实施AuRa解决方案提供实际指导和评估标准.
主要方法:
- 专注于AI/ML组件用于视觉到音频信息传输.
- 讨论当前的实际实施选择.
- 总结了Aura系统的关键评估方面.
主要成果:
- 人工智能/机器学习为实时视觉到音频翻译提供了可行的途径.
- 确定了实际的实施选择和评估指标.
- 在社会和公共卫生环境中,AuRa具有多样化的潜在应用.
结论:
- 人工智能/ML驱动的可听现实 (AuRa) 可以显著改善视力障碍者的可访问性.
- 对于AuRa解决方案,鼓励进一步开发和社区参与.
- AuRa有望产生更广泛的社会和公共卫生影响.
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