# Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19

arXiv: https://arxiv.org/abs/2210.14085 · HTML: https://mlsanity.com/paper/2210.14085/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [Very Deep Self-Attention Networks for End-to-End Speech Recognition](https://mlsanity.com/paper/1904.13377/) (2019)
- [Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks](https://mlsanity.com/paper/1910.10387/) (2019)

## Built on by (most cited first, 4 of 4)

- [Pretrained audio neural networks for Speech emotion recognition in Portuguese](https://mlsanity.com/paper/2210.14716/) (2022)
- [Acoustic models of Brazilian Portuguese Speech based on Neural Transformers](https://mlsanity.com/paper/2312.09265/) (2023)
- [Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese](https://mlsanity.com/paper/2405.17569/) (2024)
- [Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation](https://mlsanity.com/paper/2407.20989/) (2024)

## Abstract (arXiv)

This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work citespira2021 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.