吸烟状态的数字声乐生物标志物使用生态音频录音:来自Colive语音研究的结果
Hanin Ayadi1, Abir Elbéji1,2, Vladimir Despotovic3
1Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
研究人员开发了一种新的数字语音生物标志物,使用语音分析来识别吸烟者. 这种可扩展的工具准确地检测吸烟状况,特别是在女性中,有助于临床和流行病学研究.
科学领域:
- 生物医学工程 生物医学工程
- 公共卫生 公共卫生
- 语音科学 语言科学
背景情况:
- 吸烟对健康,社会和经济造成严重影响.
- 吸烟会影响语音的产生,需要更好的数据收集方法.
- 现有的吸烟状况评估方法可以提高可扩展性和准确性.
研究的目的:
- 开发一种特定于性别和语言的数字声声生物标志物来确定吸烟状态.
- 用语音特征来区分吸烟者和不吸烟者.
- 创建一个快速和可扩展的工具,用于在研究中评估吸烟状态.
主要方法:
- 利用了来自Colive Voice研究的数据.
- 应用统计分析来量化吸烟对语音的影响.
- 使用语音特征提取和机器学习来开发生物标志物 (英语和法语).
主要成果:
- 包括1332名参与者 (50%吸烟者,50%从未吸烟者).
- 观察到女性吸烟者的声音特征有显著差异 (低F0,形式,HNR).
- 获得的预测准确度为0.71和AUC为0.76的女性;0.65和0.68的男性.
结论:
- 吸烟明显影响了语音特征.
- 开发了一种用于评估吸烟状态的新型数字声乐生物标志物.
- 生物标志物为研究中的生态音频录音提供了一个快速,可扩展和准确的方法.
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