# The voice of COVID-19: Acoustic correlates of infection

arXiv: https://arxiv.org/abs/2012.09478 · HTML: https://mlsanity.com/paper/2012.09478/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data](https://mlsanity.com/paper/2104.11629/) (2021)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges](https://mlsanity.com/paper/2107.14549/) (2021)
- [Interpretability Analysis of Deep Models for COVID-19 Detection](https://mlsanity.com/paper/2211.14372/) (2022)
- [End-2-End COVID-19 Detection from Breath & Cough Audio](https://mlsanity.com/paper/2102.08359/) (2021)
- [A Summary of the ComParE COVID-19 Challenges](https://mlsanity.com/paper/2202.08981/) (2022)
- [Sustained Vowels for Pre- vs Post-Treatment COPD Classification](https://mlsanity.com/paper/2406.06355/) (2024)
- [COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection](https://mlsanity.com/paper/2206.11045/) (2022)
- [Impact of data-splits on generalization: Identifying COVID-19 from cough and context](https://mlsanity.com/paper/2106.03851/) (2021)
- [The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints](https://mlsanity.com/paper/2608.07980/) (2026)

## Abstract (arXiv)

COVID-19 is a global health crisis that has been affecting many aspects of our daily lives throughout the past year. The symptomatology of COVID-19 is heterogeneous with a severity continuum. A considerable proportion of symptoms are related to pathological changes in the vocal system, leading to the assumption that COVID-19 may also affect voice production. For the very first time, the present study aims to investigate voice acoustic correlates of an infection with COVID-19 on the basis of a comprehensive acoustic parameter set. We compare 88 acoustic features extracted from recordings of the vowels /i:/, /e:/, /o:/, /u:/, and /a:/ produced by 11 symptomatic COVID-19 positive and 11 COVID-19 negative German-speaking participants. We employ the Mann-Whitney U test and calculate effect sizes to identify features with the most prominent group differences. The mean voiced segment length and the number of voiced segments per second yield the most important differences across all vowels indicating discontinuities in the pulmonic airstream during phonation in COVID-19 positive participants. Group differences in the front vowels /i:/ and /e:/ are additionally reflected in the variation of the fundamental frequency and the harmonics-to-noise ratio, group differences in back vowels /o:/ and /u:/ in statistics of the Mel-frequency cepstral coefficients and the spectral slope. Findings of this study can be considered an important proof-of-concept contribution for a potential future voice-based identification of individuals infected with COVID-19.