诊断病态的语言与简化的长期短期记忆学习功能简化特征的诊断
Tuan D Pham1, Simon B Holmes1, Lifong Zou1
1Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, Turner Street, E1 2AD, London, UK.
Computers in biology and medicine
|January 14, 2024
概括
这项研究引入了新的AI功能,用于使用短语音信号诊断病态语音. 这种新的方法实现了90%的准确性,改善了语音障碍评估和患者护理.
科学领域:
- 人工智能在医学中的应用
- 语音病理学诊断诊断 语音病理学诊断
- 生物医学信号处理
背景情况:
- 准确的病理语音诊断对于有效治疗和改善患者生活质量至关重要.
- 全球语音障碍的发病率不断上升,需要有效可靠的诊断工具.
- 语音病理学的先进研究对于开发更好的干预策略至关重要.
研究的目的:
- 为深度学习引入新的功能,用于分析短语音信号以检测病态语音.
- 提高语音疾病诊断程序的精度和可靠性.
- 为了使语音障碍的治疗方法更有针对性.
主要方法:
- 纳入时空和时间频率特征用于深度学习分析.
- 利用长短期内存 (LSTM) 网络进行语音信号分析.
- 采用数据平衡策略与公开可用的语音数据库.
主要成果:
- 实现了90%的准确性,93%的灵敏度,87%的特异性,88%的精度,F1得分为0.90.
- 证明ROC曲线下的高面积为0.96.
- 使用波段时间散射系数和其他特征类型的现有方法的性能优于现有方法.
结论:
- 时间频率和时间空间特征显示出对人工智能驱动的语音病理学诊断的重大前景.
- 拟议的方法可以提高准确性,并实现实时病理语音评估.
- 促进了针对言语障碍患者的更有针对性和更有效的治疗干预措施.
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