Akman Alican, Coppock Harry, Gaskell Alexander, Tzirakis Panagiotis, Jones Lyn, Schuller Björn W
GLAM-Group on Language, Audio, and Music, Imperial College London, London, United Kingdom.
Department of Radiology, North Bristol NHS Trust, Bristol, United Kingdom.
Front Digit Health. 2022 Jul 7;4:789980. doi: 10.3389/fdgth.2022.789980. eCollection 2022.
Several machine learning-based COVID-19 classifiers exploiting vocal biomarkers of COVID-19 has been proposed recently as digital mass testing methods. Although these classifiers have shown strong performances on the datasets on which they are trained, their methodological adaptation to new datasets with different modalities has not been explored. We report on cross-running the modified version of 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-19-positive or COVID-19-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. We also present the results of the cross dataset experiments with CIdeR that show the limitations of using the current COVID-19 datasets jointly to build a collective COVID-19 classifier.
最近，作为数字大规模检测方法，已经提出了几种基于机器学习的利用COVID-19声音生物标志物的COVID-19分类器。尽管这些分类器在其训练的数据集上表现出了强大的性能，但尚未探索它们对具有不同模态的新数据集的方法适应性。我们报告了在两个来自咳嗽和语音音频挑战的2021年Interspeech COVID-19诊断数据集：ComParE和DiCOVA上对最近的COVID-19识别残差网络（CIdeR）修改版本的交叉运行情况。CIdeR是一个端到端的深度学习神经网络，最初设计用于根据来自一个已发布的众包数据集的咳嗽和呼吸音频记录对个体是否为COVID-19阳性或COVID-19阴性进行分类。在当前研究中，我们展示了CIdeR在2021年Interspeech的COVID-19咳嗽和语音子挑战ComParE和DiCOVA的二元COVID-19诊断中的潜力。CIdeR相对于几个基线有显著改进。我们还展示了CIdeR的跨数据集实验结果，这些结果显示了联合使用当前COVID-19数据集构建一个集体COVID-19分类器的局限性。
