查语音障碍:声学语音质量指数,头峰突出度和机器学习
Ahmed M Yousef1,2,3, Adrián Castillo-Allendes3,4, Mark L Berardi3
1Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, Massachusetts, USA.
光滑的头顶突出 (CPPs) 有效地检测到美国英语发言者的语音障碍,提供了一个实用的临床工具. 虽然机器学习显示出潜力,但CPP为语音质量评估提供了平衡和可访问的方法.
科学领域:
- 语音和听力科学 语言和听力科学
- 医学声学 医学声学
- 计算语言学 计算语言学
背景情况:
- 声学语音质量指数 (AVQI) 和光滑脑膜峰突出度 (CPPs) 是声质评估的既定措施.
- 他们的诊断准确性在检测美国英语发言者的语音障碍需要进一步的评估.
- 与机器学习 (ML) 模型的比较对于理解它们的临床实用性至关重要.
研究的目的:
- 评估AVQI-3和CPPs的诊断准确性,以识别美国英语发言者的语音障碍.
- 将AVQI-3和CPP的性能与各种机器学习模型进行比较.
- 确定临床语音质量评估的最实用和最有效的措施.
主要方法:
- 对187名参与者 (138名有语音障碍,49名健康) 的回顾性研究.
- 使用VOXplot软件对AVQI-3和CPP进行持续元音和运行语音样本的分析.
- 训练和比较四个ML模型 (随机森林,k-NN,SVM,决策树) 使用ROC曲线和Youden指数.
主要成果:
- 确定了最佳切断分数:AVQI-3在1.54 (55%的灵敏度,80%的特异性) 和CPPs在14.35dB (65%的灵敏度,78%的特异性).
- 与AVQI-3相比,CPP显示出更高的灵敏度和特异性的平衡.
- CPP的性能与ML模型的平均性能非常相匹配,表现优于AVQI-3.
结论:
- 机器学习模型对语音障碍诊断有希望,但需要进一步开发以实现概括性和可解释性.
- AVQI-3和CPP仍然是临床语音质量评估的实用和可访问的工具.
- CPP在识别语音障碍方面具有显著的优势,使其成为资源有限的诊所推的选择.
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