人工智能支持 AAC 图形符号适应
E A Draffan1, Mike Wald1, Chaohai Ding1
1University of Southampton, UK.
Studies in health technology and informatics
|August 28, 2023
概括
生成型人工智能可以为增强型和替代型通信 (AAC) 用户创建个性化的图形符号,并将现有的风格适应个人需求. 这项技术增强了符号的灵活性和可访问性,为不同的用户.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 辅助技术 辅助技术 辅助技术
背景情况:
- 技术进步使图像和文本操纵能够用于各种应用.
- 目前用于增强和替代通信 (AAC) 的符号集缺乏个性化和灵活性.
- 生成性AI为定制符号创建提供了潜力.
研究的目的:
- 探索使用生成人工智能来创建个性化的图形符号.
- 调整开放许可符号以适应个人用户风格和当地环境.
- 通过利益相关者的反来评估人工智能生成符号的有效性.
主要方法:
- 使用图像对图像和文本对图像生成AI模型.
- 根据用户定义的风格调整现有的开放许可符号集.
- 进行为期六个月的试点研究,包括符号生成和利益相关者评估.
- 实施一个在线投票系统来评估结果.
主要成果:
- 通过使用人工智能成功生成了适应符号的集合.
- 启动了一项试点研究,以调整开放许可符号.
- 建立了一个框架,用于利益相关者对人工智能生成的符号进行评估.
结论:
- 生成型人工智能在AAC中拥有个性化符号生成的巨大潜力.
- 人工智能驱动的符号适应可以提高AAC用户的灵活性和用户体验.
- 未来的工作重点是将成功的符号集成到现有的AAC系统中.
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