Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges
Autor: | Akman, Alican, Coppock, Harry, Gaskell, Alexander, Tzirakis, Panagiotis, Jones, Lyn, Schuller, Björn W. |
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Rok vydání: | 2021 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: 5 pages, 1 figure |
Databáze: | arXiv |
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