论文标题
通过分析声带振荡来检测COVID-19
Detection of COVID-19 through the analysis of vocal fold oscillations
论文作者
论文摘要
发声或声带的振动是人类发声声音产生的发声的主要来源。这是一个复杂的生物力学过程，对说话者呼吸参数的变化高度敏感。由于大多数有症状的COVID 19例均具有中度至重度呼吸功能损害，因此我们可以假设可以通过检查声带的振动来观察到Covid-19的特征。我们的目标是验证这一假设，并定量地表征观察到的变化，以便从语音中检测Covid-19。为此，我们使用动力学系统模型进行声带的振荡，并使用我们最近开发的ADLES算法对其进行解决，以直接从记录的语音中产生声带振荡模式。 COVID-19的临床策划数据集正面和负面受试者的实验结果揭示了与COVID-19相关的声带振荡的特征模式。我们表明，这些都是突出的和歧视性的，即使是简单的分类器（例如逻辑回归），也仅使用孤立的扩展元音的记录就可以产生高检测精度。
Phonation, or the vibration of the vocal folds, is the primary source of vocalization in the production of voiced sounds by humans. It is a complex bio-mechanical process that is highly sensitive to changes in the speaker's respiratory parameters. Since most symptomatic cases of COVID-19 present with moderate to severe impairment of respiratory functions, we hypothesize that signatures of COVID-19 may be observable by examining the vibrations of the vocal folds. Our goal is to validate this hypothesis, and to quantitatively characterize the changes observed to enable the detection of COVID-19 from voice. For this, we use a dynamical system model for the oscillation of the vocal folds, and solve it using our recently developed ADLES algorithm to yield vocal fold oscillation patterns directly from recorded speech. Experimental results on a clinically curated dataset of COVID-19 positive and negative subjects reveal characteristic patterns of vocal fold oscillations that are correlated with COVID-19. We show that these are prominent and discriminative enough that even simple classifiers such as logistic regression yields high detection accuracies using just the recordings of isolated extended vowels.
