通过语音分析检测到的睡眠不足
Etienne Thoret1,2,3, Thomas Andrillon4,5,6, Caroline Gauriau5,6
1Laboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France.
PLoS computational biology
|February 5, 2024
概括
自动语音分析现在可以使用新的声学功能来检测睡眠不足. 这种方法可以揭示个体的声音变化,为客观的睡眠评估提供了潜在的工具.
科学领域:
- 神经科学是一个神经科学.
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 睡眠不足对个人和社会产生重大影响.
- 目前用于检测睡眠不足的方法缺乏速度和客观性.
- 需要对睡眠剥夺有客观的声乐生物标志物.
研究的目的:
- 开发和验证一个客观的方法来检测睡眠剥夺使用自动语音分析.
- 提高机器学习模型的可解释性,以检测睡眠不足.
- 为了识别与睡眠剥夺相关的特定声音变化.
主要方法:
- 利用语音录音的自动声学分析.
- 采用基于光谱-时间调制分析的通用听觉表征.
- 应用了对模型可解释性的反向相关性.
- 与最先进的语音功能进行性能比较.
主要成果:
- 一般的听觉特征成功地检测出睡眠不足,其准确性与现有方法相美.
- 确定了两种不同的声效:表情的变化 (缓慢的时间调制) 和语音质量 (光谱特征).
- 在这些声效应的平衡中观察到显著的个体变化.
结论:
- 自动语音分析提供了一种可行,客观的方法来检测睡眠不足.
- 声乐生物标志物反映了睡眠剥夺的明确 (主观嗜睡) 和隐含后果.
- 睡眠剥夺的个性化声乐标志物需要进一步研究,以获得临床应用.
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