  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.
