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人工智能启用智能口罩用于语音识别,用于未来的听力设备.

Hira Hameed1, Lubna1,2, Muhammad Usman3

  • 1James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.

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此摘要是机器生成的。

这项研究引入了一种基于射频识别 (RFID) 的新型智能口罩,用于唇读,即使使用面罩也可以识别语音. 该RFID系统达到80%的准确性,提高了听力障碍者的可访问性.

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科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 人与计算机的交互

背景情况:

  • 唇读技术面临着诸如遮蔽,照明问题,隐私问题和不适等限制.
  • 由于COVID-19大流行和面具的广泛使用,目前的基于视觉和可穿戴的唇读系统变得无效.
  • 现有的方法在与现实世界的可用性和听力障碍者可访问性作斗争.

研究的目的:

  • 开发一个有效的唇读框架,克服现有技术的局限性,特别是在面罩下.
  • 引入基于射频识别 (RFID) 的智能口罩,用于非侵入性和准确的唇部运动分析.
  • 提高听力障碍者语音识别和对话可访问性.

主要方法:

  • 一个智能RFID面罩被设计并用于收集基于唇部运动的语音数据.
  • 使用RFID口罩创建了一个包含元音 (A,E,I,O,U),辅音 (F,G,M,S) 和特定单词的数据集.
  • 收集的RFID数据使用已建立的机器学习模型进行处理和分类,包括随机森林.

主要成果:

  • 基于RFID的唇读系统在个人和组合数据集中显示出高分类准确性.
  • 随机森林模型在组合的RFID数据集上实现了80%的显著分类准确性.
  • 拟议的系统成功启用了面罩下的唇读,验证了其有效性.

结论:

  • 基于RFID的智能口罩为读唇的挑战提供了可行的解决方案,特别是在需要戴面罩的环境中.
  • 这项技术显著提高了语音识别能力,并促进了听力障碍者更大的对话可访问性.
  • 该研究强调了射频 (RF) 传感在开发下一代辅助通信设备方面的潜力.