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

• URL: http://arxiv.org/abs/2204.04802v1
• Date: Mon, 11 Apr 2022 00:19:14 GMT
• Title: On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice
• Authors: Ankit Shah, Hira Dhamyal, Yang Gao, Rita Singh, Bhiksha Raj
• Abstract summary: We show that detecting COVID-19 from voice does not require custom-made non-standard features or complicated neural network classifiers. We demonstrate this from a human-curated dataset collected and calibrated in clinical settings.
• Score: 34.553128768223615
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 degrees 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 and also more computationally efficient in that they can be run locally on small devices. We demonstrate this from a human-curated dataset collected and calibrated in clinical settings. On this dataset which comprises over 1000 speakers, a simple binary classifier is able to achieve 94% detection accuracy.

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