On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice

In a self-assesment study, COVID patients reported difficulty producing certain voiced sounds and noticed changes in Lately, there has been a global effort by multiple research groups their voice [8]. to detect COVID-19 from voice. Different researchers use different Consequently, a number of research groups around the world kinds of information from the voice signal to achieve this. Various have initiated efforts on attempting to diagnose potential Covid infections types of phonated sounds and the sound of cough and breath have from recordings of vocalizations [9, 5]. While most groups all been used with varying degree of success in automated voice have focused on cough sounds [10, 11, 12] as they are a frequent based COVID-19 detection apps. In this paper, we show that detecting symptom of Covid-19, several groups have also considered other COVID-19 from voice does not require custom made nonstandard vocalizations, such as breathing sounds [10, 13] extended vowels features or complicated neural network classifiers rather it [14, 15, 16], and counts. Yet other teams have analyzed free-form can be successfully done with just standard features and simple binary speech such as those obtainable from YouTube recordings[17].