  This work presents an outer product-based approach to fuse the embedded
representations generated from the spectrograms of cough, breath, and speech
samples for the automatic detection of COVID-19. To extract deep learnt
representations from the spectrograms, we compare the performance of a CNN
trained from scratch and a ResNet18 architecture fine-tuned for the task at
hand. Furthermore, we investigate whether the patients' sex and the use of
contextual attention mechanisms is beneficial. Our experiments use the dataset
released as part of the Second Diagnosing COVID-19 using Acoustics (DiCOVA)
Challenge. The results suggest the suitability of fusing breath and speech
information to detect COVID-19. An Area Under the Curve (AUC) of 84.06% is
obtained on the test partition when using a CNN trained from scratch with
contextual attention mechanisms. When using the ResNet18 architecture for
feature extraction, the baseline model scores the highest performance with an
AUC of 84.26%.
