# Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data

arXiv: https://arxiv.org/abs/2402.07619 · HTML: https://mlsanity.com/paper/2402.07619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [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)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data](https://mlsanity.com/paper/2102.05225/) (2021)
- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1706.07156/) (2017)
- [Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS](https://mlsanity.com/paper/2011.10470/) (2020)
- [Exploring auditory acoustic features for the diagnosis of the Covid-19](https://mlsanity.com/paper/2201.09110/) (2022)

## Built on by (most cited first, 1 of 1)

- [Optimising MFCC parameters for the automatic detection of respiratory diseases](https://mlsanity.com/paper/2408.07522/) (2024)

## Abstract (arXiv)

COVID-19 has affected more than 223 countries worldwide and in the Post-COVID Era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4352 participants via a COVID-19 Sounds app. Voice features including Mel-spectrograms and Mel-frequency cepstral coefficients (MFCC) and CNN Encoder features are extracted. Based on the voice data, we develop deep learning classification models to detect COVID-19 cases. These models include Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) and Hidden-Unit BERT (HuBERT). We compare their predictive power to baseline machine learning models. HuBERT achieves the highest accuracy of 86% and the highest AUC of 0.93. The results achieved with the proposed models suggest promising results in COVID-19 diagnosis from voice recordings when compared to the results obtained from the state-of-the-art.