Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19
This work addresses the need for non-invasive detection of respiratory issues in COVID-19 patients, representing an incremental improvement over existing methods.
The paper tackled the problem of detecting respiratory insufficiency in COVID-19 patients by analyzing speech samples, achieving an improved accuracy of 96.53% using Transformer neural networks compared to a previous convolutional neural network method.
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.