  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.
