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arXiv · 2204.04802

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

Ankit Shah·Hira Dhamyal·Yang Gao·Daniel Arancibia·Mario Arancibia·Bhiksha Raj·Rita Singh
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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 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.

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Ankit Shah, Hira Dhamyal, Yang Gao, Daniel Arancibia, Mario Arancibia, Bhiksha Raj, Rita Singh. 2022-10-25. On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice. https://arxiv.org/abs/2204.04802

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