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

arXiv: https://arxiv.org/abs/2204.04802 · HTML: https://mlsanity.com/paper/2204.04802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Multi-task self-supervised learning for Robust Speech Recognition](https://mlsanity.com/paper/2001.09239/) (2020)
- [Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks](https://mlsanity.com/paper/1904.03416/) (2019)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds](https://mlsanity.com/paper/2009.08790/) (2020)
- [COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis](https://mlsanity.com/paper/2003.11117/) (2020)
- [Detection of COVID-19 through the analysis of vocal fold oscillations](https://mlsanity.com/paper/2010.10707/) (2020)
- [Interpreting glottal flow dynamics for detecting COVID-19 from voice](https://mlsanity.com/paper/2010.16318/) (2020)
- [COVID-19 Detection Using Recorded Coughs in the 2021 DiCOVA Challenge](https://mlsanity.com/paper/2105.10619/) (2021)
- [An Overview of Techniques for Biomarker Discovery in Voice Signal](https://mlsanity.com/paper/2110.04678/) (2021)

## Abstract (arXiv)

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.