Automated voice-based detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could facilitate the screening for COVID19. A dataset of cellular phone recordings from 88 subjects was recently collected. The dataset included vocal utterances, speech and coughs that were self-recorded by the subjects in either hospitals or isolation sites. All subjects underwent nasopharyngeal swabbing at the time of recording and were labelled as SARS-CoV-2 positives or negative controls. The present study harnessed deep machine learning and speech processing to detect the SARS-CoV-2 positives. A three-stage architecture was implemented. A self-supervised attention-based transformer generated embeddings from the audio inputs. Recurrent neural networks were used to produce specialized sub-models for the SARS-CoV-2 classification. An ensemble stacking fused the predictions of the sub-models. Pre-training, bootstrapping and regularization techniques were used to prevent overfitting. A recall of 78% and a probability of false alarm (PFA) of 41% were measured on a test set of 57 recording sessions. A leave-one-speaker-out cross validation on 292 recording sessions yielded a recall of 78% and a PFA of 30%. These preliminary results imply a feasibility for COVID19 screening using voice.

中文翻译：

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通过语音检测 SARS-CoV-2

基于语音的严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 的自动检测可以促进 COVID19 的筛查。最近收集了来自 88 名受试者的手机录音数据集。该数据集包括由受试者在医院或隔离场所自我记录的发声、言语和咳嗽。所有受试者在记录时都接受了鼻咽拭子，并被标记为 SARS-CoV-2 阳性或阴性对照。本研究利用深度机器学习和语音处理来检测 SARS-CoV-2 阳性。实施了三阶段架构。一个自我监督的基于注意力的转换器从音频输入中生成嵌入。循环神经网络被用于生成用于 SARS-CoV-2 分类的专门子模型。集成堆叠融合了子模型的预测。预训练、引导和正则化技术用于防止过度拟合。在 57 个记录会话的测试集上测量到 78% 的召回率和 41% 的误报概率 (PFA)。对 292 次录音会话进行的留一个发言者交叉验证产生了 78% 的召回率和 30% 的 PFA。这些初步结果表明使用语音进行 COVID19 筛查的可行性。