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.

翻译：我们报告了最近COVID-19识别网(CIdeR)的交叉运行情况,报告了2021年COVID-19对咳嗽和言语声响挑战的两次Interspeech 2021 COVID-19诊断:ComParE和DiCOVA。 CIDER是一个端到端的深层学习神经网络,最初旨在根据已公布的众源数据集的咳嗽和呼吸录音,对一个人是COVID阳性还是COVID阴性进行分类。在本次研究中,我们展示了COVID-19 Cough和COVA的发言次挑战的二进式诊断中CIDER的潜力。 CIDER在若干基线上取得了显著进步。