论文标题
AUDIO MFCC-GRAM TRUNSSITER用于COVID-19的呼吸不足检测
Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19
论文作者
论文摘要
这项工作探讨了语音作为生物标志物，并通过分析语音样本来研究呼吸不足（RI）的检测。先前的工作\ cite {spira2021}构建了一个呼吸功能不全19的数据集，并通过卷积神经网络进行了分析，该数据的准确度达到了87.04美元\％$ $，从而验证了一个人可以通过演讲来检测RI的假设。在这里，我们研究了变压器神经网络架构如何改善RI检测的性能。这种方法可以构建声学模型。通过选择正确的预处理技术，我们生成了一个自我监督的声学模型，从而改善了RI检测的变压器的性能（$ 96.53 \％$）。
This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and analyzed it by means of a convolutional neural network achieving an accuracy of $87.04\%$, validating the hypothesis that one can detect RI through speech. Here, we study how Transformer neural network architectures can improve the performance on RI detection. This approach enables construction of an acoustic model. By choosing the correct pretraining technique, we generate a self-supervised acoustic model, leading to improved performance ($96.53\%$) of Transformers for RI detection.
