论文标题
关于使用二进制分类器而不是数据密集的神经网络分类器从语音中检测Covid-19的实用主义
On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice
论文作者
论文摘要
最近，多个研究小组一直在全球努力从语音中检测Covid-19。不同的研究人员使用语音信号中的不同类型的信息来实现这一目标。各种类型的发声声音和咳嗽和呼吸的声音都在基于自动语音的Covid-19检测应用程序中取得了不同程度的成功。在本文中，我们表明，从语音中检测COVID-19不需要定制的非标准功能或复杂的神经网络分类器，而是仅仅使用标准功能和简单的二进制分类器就可以成功完成。实际上，我们表明后者不仅更准确，更容易解释，而且在计算上也更有效，因为它们可以在小型设备上本地运行。我们在临床环境中收集和校准的1000多名受试者的人类策划数据集上证明了这一点。
Lately, there has been a global effort by multiple research groups to detect COVID-19 from voice. Different researchers use different kinds of information from the voice signal to achieve this. Various types of phonated sounds and the sound of cough and breath have all been used with varying degree of success in automated voice-based COVID-19 detection apps. In this paper, we show that detecting COVID-19 from voice does not require custom-made non-standard features or complicated neural network classifiers rather it can be successfully done with just standard features and simple binary classifiers. In fact, we show that the latter is not only more accurate and interpretable but also more computationally efficient in that they can be run locally on small devices. We demonstrate this on a human-curated dataset of over 1000 subjects, collected and calibrated in clinical settings.
