论文标题
社交焦虑的声音签名：使用假设检验和机器学习方法探索
The Vocal Signature of Social Anxiety: Exploration using Hypothesis-Testing and Machine-Learning Approaches
论文作者
论文摘要
背景 - 社交焦虑（SA）是一种常见且令人衰弱的状况，即使在亚诊断阈值下，也会对生活质量产生负面影响。我们试图使用假设检验和机器学习（ML）方法来表征SA的声学签名。方法 - 参与者形成了自发的话语，以响应拒绝或同意所谓同龄人的命令的指示。分析了这些话语的声音特性（例如强度和持续时间）。结果 - 我们的预测是，与低sa（n = 31）相比，高-SA（n = 32）个体表现出较不自信的声音语音签名，尤其是在拒绝话语方面，只有经典的假设测试方法得到了部分支持。但是，ML分析的结果，特别是决策树分类器与SA中的这种语音模式一致。使用高斯工艺（GP）分类器，我们能够区分具有高度（75.6％）精度和良好（.83 AUC）可分离性的高-SA和低SA个体。我们还期望并发现声带属性在拒绝和同意话语之间有所不同。结论 - 我们的发现为ML方法在研究心理病理学研究的有用性提供了进一步的支持，强调了开发自动技术以创建SAD的行为标志的实用性。临床上，这些程序的简单性和可访问性可能会鼓励人们寻求专业帮助。
Background - Social anxiety (SA) is a common and debilitating condition, negatively affecting life quality even at sub-diagnostic thresholds. We sought to characterize SA's acoustic signature using hypothesis-testing and machine learning (ML) approaches. Methods - Participants formed spontaneous utterances responding to instructions to refuse or consent to commands of alleged peers. Vocal properties (e.g., intensity and duration) of these utterances were analyzed. Results - Our prediction that, as compared to low-SA (n=31), high-SA (n=32) individuals exhibit a less confident vocal speech signature, especially with respect to refusal utterances, was only partially supported by the classical hypothesis-testing approach. However, the results of the ML analyses and specifically the decision tree classifier were consistent with such speech patterns in SA. Using a Gaussian Process (GP) classifier, we were able to distinguish between high- and low-SA individuals with high (75.6%) accuracy and good (.83 AUC) separability. We also expected and found that vocal properties differentiated between refusal and consent utterances. Conclusions - Our findings provide further support for the usefulness of ML approach for the study of psychopathology, highlighting the utility of developing automatic techniques to create behavioral markers of SAD. Clinically, the simplicity and accessibility of these procedures may encourage people to seek professional help.
