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arXiv · 2107.14549

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

Alican Akman·Harry Coppock·Alexander Gaskell·Panagiotis Tzirakis·Lyn Jones·Björn W. Schuller
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Abstract

We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neural network originally designed to classify whether an individual is COVID-positive or COVID-negative based on coughing and breathing audio recordings from a published crowdsourced dataset. In the current study, we demonstrate the potential of CIdeR at binary COVID-19 diagnosis from both the COVID-19 Cough and Speech Sub-Challenges of INTERSPEECH 2021, ComParE and DiCOVA. CIdeR achieves significant improvements over several baselines.

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Alican Akman, Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Lyn Jones, Björn W. Schuller. 2021-07-30. Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges. https://arxiv.org/abs/2107.14549

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