作为疲劳的语音生物标志物,声比率:K-最近邻近机器学习算法
Savita Gaur1, Priti Kalani2, M Mohan3
1Scientist 'E' (Neurophysiology), DIPAS, DRDO, Timarpur, Delhi, India.
Medical journal, Armed Forces India
|December 30, 2024
概括
语音中的声比 (HNR) 可以在睡眠不足后检测到疲劳. 这种用机器学习分析的语音生物标志物有效地区分正常和疲劳的声音,有助于疲劳诊断.
科学领域:
- 语音科学是一种语言科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 睡眠不足后,声音质量的变化是可以注意到的.
- 循环节律的破坏导致疲劳,影响言语.
- 作为一个潜在的语音生物标志物,声比率 (HNR) 被探索.
研究的目的:
- 评估HNR在睡眠剥夺后分辨疲劳和正常声音的有效性.
- 调查HNR作为一种用于检测疲劳的语音生物标志物.
主要方法:
- 从32名健康的印度年轻男性身上记录了持续发音 /a/ 的声学样本.
- 实施了一晚的睡眠剥夺.
- 使用MATLAB统计技术和k-最近邻居 (KNN) 机器学习算法来分析HNR.
主要成果:
- 在睡眠剥夺后,在凌晨3点 (p<0.05) 观察到声音特征的显著变化,特别是HNR.
- 在KNN分类器成功地区分了正常和疲劳的语音样本.
- HNR证明了其作为生物标志物的有效性,用于检测因疲劳而引起的声音变化.
结论:
- HNR可以将睡眠不足引起的疲劳与声乐变化联系起来.
- 该方法使用KNN分类,为诊断疲劳提供了额外的声学生物标志物.
- HNR分析为客观的疲劳评估提供了有价值的工具.
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