The DiCOVA 2021 challenge - An encoder-decoder approach for COVID-19 recognition from coughing audio

This paper presents the automatic recognition of COVID-19 from coughing. In particular, it describes our contribution to the DiCOVA challenge - Track 1, which addresses such cough sound analysis for COVID-19 detection. Pathologically, the effects of a COVID-19 infection on the respiratory system and on breathing patterns are known. We demonstrate the use of breathing patterns of the cough audio signal in identifying the COVID-19 status. Breathing patterns of the cough audio signal are derived using a model trained with the subset of the UCL Speech Breath Monitoring (UCL-SBM) database. This database provides speech recordings of the participants while their breathing values are captured by a respiratory belt. We use an encoder-decoder architecture. The encoder encodes the audio signal into breathing patterns and the decoder decodes the COVID-19 status for the corresponding breathing patterns using an attention mechanism. The encoder uses a pre-trained model which predicts breathing patterns from the speech signal, and transfers the learned patterns to cough audio signals. With this architecture, we achieve an AUC of 64:42% on the evaluation set of Track 1.