# Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese

arXiv: https://arxiv.org/abs/2405.17569 · HTML: https://mlsanity.com/paper/2405.17569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 9 of 9)

- [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)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [SSAST: Self-Supervised Audio Spectrogram Transformer](https://mlsanity.com/paper/2110.09784/) (2021)
- [Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19](https://mlsanity.com/paper/2210.14085/) (2022)
- [Pretrained audio neural networks for Speech emotion recognition in Portuguese](https://mlsanity.com/paper/2210.14716/) (2022)
- [Interpretability Analysis of Deep Models for COVID-19 Detection](https://mlsanity.com/paper/2211.14372/) (2022)

## Built on by (most cited first, 1 of 1)

- [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 investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved 96.5% accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types.