# An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

arXiv: https://arxiv.org/abs/2005.00096 · HTML: https://mlsanity.com/paper/2005.00096/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough](https://mlsanity.com/paper/2107.10716/) (2021)
- [Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data](https://mlsanity.com/paper/2104.02005/) (2021)
- [Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution](https://mlsanity.com/paper/2209.00652/) (2022)
- [L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data](https://mlsanity.com/paper/2205.07682/) (2022)
- [Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice](https://mlsanity.com/paper/2112.07285/) (2021)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- [Fitbeat: COVID-19 Estimation based on Wristband Heart Rate](https://mlsanity.com/paper/2104.09263/) (2021)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)
- [Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data](https://mlsanity.com/paper/2307.02975/) (2023)

## Abstract (arXiv)

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.