  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.
