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We report on cross-running the recent COVID-19 Identification ResNet (CIdeR)on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audiochallenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neuralnetwork originally designed to classify whether an individual is COVID-positiveor COVID-negative based on coughing and breathing audio recordings from apublished crowdsourced dataset. In the current study, we demonstrate thepotential of CIdeR at binary COVID-19 diagnosis from both the COVID-19 Coughand Speech Sub-Challenges of INTERSPEECH 2021, ComParE and DiCOVA. CIdeRachieves significant improvements over several baselines.

Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges