论文标题
COVID-19从电话质量语音数据中检测到患者检测
COVID-19 Patient Detection from Telephone Quality Speech Data
论文作者
论文摘要
在本文中，我们试图研究语音数据中有关COVID-19疾病的线索的存在。我们使用类似于说话者识别的方法。每个句子都表示为每个音素的短期MEL滤波器库特征的超级向量。这些功能用于学习两级分类器，以将Covid-19语音与正常人分开。从YouTube视频收集的小数据集上的实验表明，该数据集上的SVM分类器能够达到88.6％的精度和92.7％的F1分数。进一步的调查表明，一些电话课，例如鼻腔，停止和中元元音，可以比其他类别更好地区分这两个类别。
In this paper, we try to investigate the presence of cues about the COVID-19 disease in the speech data. We use an approach that is similar to speaker recognition. Each sentence is represented as super vectors of short term Mel filter bank features for each phoneme. These features are used to learn a two-class classifier to separate the COVID-19 speech from normal. Experiments on a small dataset collected from YouTube videos show that an SVM classifier on this dataset is able to achieve an accuracy of 88.6% and an F1-Score of 92.7%. Further investigation reveals that some phone classes, such as nasals, stops, and mid vowels can distinguish the two classes better than the others.
