基于来自鼻腔和口腔通道的声学信号,自动检测长缺陷系统
Yu Zhang1, Jing Zhang1, Wen Li1
1College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
Diagnostics (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种AI系统,用于检测发缺陷 (VPI),一种语言障碍. 非侵入性技术达到98.52%的准确性,为语言治疗师的短缺提供了解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 语音科学 语言科学
- 人工智能的人工智能
背景情况:
- 耳鼻缺陷 (VPI) 导致言语和吞障碍,需要专门的语言治疗.
- 全球缺乏经验丰富的语言治疗师限制了诊断和治疗的机会.
- 人工智能 (AI) 为自动化VPI检测提供了一个潜在的解决方案.
研究的目的:
- 开发一个自动的,非侵入性的系统,用于主体级VPI检测.
- 为应对语言治疗师短缺所带来的诊断挑战.
主要方法:
- 根据受损关节原理,收集了VPI患者的鼻腔和口腔声学信号.
- 为辅音 (突出频率,频率分布) 开发了语音级别的区分特征,以检测鼻空气排放.
- 提出了一个交叉注意力剩余语网络 (CARS-Net) 用于高鼻度敏感元音分类 (VPI/非VPI).
主要成果:
- 综合系统在自建数据集上实现了98.52%的高精度.
- 卡斯网络模型有效地对元音进行了分类,为整体VPI检测准确性做出了贡献.
- 拟议的特征成功地将鼻空气排放区分为辅音.
结论:
- 开发的AI系统为VPI诊断提供了一种可行,非侵入性和准确的方法.
- 这项技术可以帮助减轻语音治疗师的负担,并改善患者获得护理的机会.
- 建议进一步实施这种自动VPI诊断系统.
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