揭开社交焦虑的面纱:在对照研究中分析即兴演讲的声音和语言特征

N. K. Sahu, Manjeet Yadav, H. Lone
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摘要

及早发现和治疗社交焦虑症(SAD)至关重要。然而,目前的诊断方法有几个缺点,包括临床访谈耗时长,自我报告易受情绪偏差影响,生理测量不确定。我们的研究重点是利用从参与者 "讲话 "中提取的声学和语言特征来诊断 SAD 的数字化方法。我们的方法包括识别所提取特征与 SAD 严重程度之间的相关性,选择有效的特征,并比较经典的机器学习和深度学习方法来预测 SAD。我们的研究结果表明,如果单独考虑深度学习方法,声学特征和语言特征都优于深度学习方法。逻辑回归证明了声学特征的有效性,而随机森林则在语言特征方面表现出色,达到了 85.71% 的最高准确率。我们的研究结果为非侵入式 SAD 诊断铺平了道路,它可以方便地在任何地方使用,从而促进早期检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling Social Anxiety: Analyzing Acoustic and Linguistic Traits in Impromptu Speech within a Controlled Study
Early detection and treatment of Social Anxiety Disorder (SAD) is crucial. However, current diagnostic methods have several drawbacks, including being time-consuming for clinical interviews, susceptible to emotional bias for self-reports, and inconclusive for physiological measures. Our research focuses on a digital approach using acoustic and linguistic features extracted from participants’ “speech” for diagnosing SAD. Our methodology involves identifying correlations between extracted features and SAD severity, selecting the effective features, and comparing classical machine learning and deep learning methods for predicting SAD. Our results demonstrate that both acoustic and linguistic features outperform deep learning approaches when considered individually. Logistic Regression proves effective for acoustic features, while Random Forest excels with linguistic features, achieving the highest accuracy of 85.71%. Our findings pave the way for non-intrusive SAD diagnosing that can be used conveniently anywhere, facilitating early detection.
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