论文标题
解释用于从语音检测Covid-19的glottal流动动力学
Interpreting glottal flow dynamics for detecting COVID-19 from voice
论文作者
论文摘要
在Covid-19的发病机理中，呼吸功能的损害通常是关键症状之一。研究表明，在这些情况下，语音产生也受到不利影响 - 声带振荡是异步，不对称和在发声过程中受到限制的。本文提出了一种分析语音生产过程中glottal流动波形（GFW）的差分动力学的方法，以识别其中最重要的特征，这些功能对于从语音中检测Covid-19最重要。由于很难直接在COVID-19患者中测量它，因此我们从记录的语音信号中推断出来，并将其与根据语音的物理模型计算的GFW进行比较。对于正常声音，两者之间的差异应很小，因为构建了物理模型以在正常假设下解释发音。更大的差异暗示着有助于物理模型正确性的生物物理因素中的异常，从而间接揭示了它们的意义。我们提出的方法使用基于CNN的两步注意模型，该模型在两个GFW的差异中定位在时间功能空间中的异常，从而使我们可以推断出它们作为分类的判别特征的潜力。使用临床策划的COVID-19-19-正阳性和阴性受试者的临床策划数据集证明了该方法的生存能力。
In the pathogenesis of COVID-19, impairment of respiratory functions is often one of the key symptoms. Studies show that in these cases, voice production is also adversely affected -- vocal fold oscillations are asynchronous, asymmetrical and more restricted during phonation. This paper proposes a method that analyzes the differential dynamics of the glottal flow waveform (GFW) during voice production to identify features in them that are most significant for the detection of COVID-19 from voice. Since it is hard to measure this directly in COVID-19 patients, we infer it from recorded speech signals and compare it to the GFW computed from physical model of phonation. For normal voices, the difference between the two should be minimal, since physical models are constructed to explain phonation under assumptions of normalcy. Greater differences implicate anomalies in the bio-physical factors that contribute to the correctness of the physical model, revealing their significance indirectly. Our proposed method uses a CNN-based 2-step attention model that locates anomalies in time-feature space in the difference of the two GFWs, allowing us to infer their potential as discriminative features for classification. The viability of this method is demonstrated using a clinically curated dataset of COVID-19 positive and negative subjects.
