# An Overview of Techniques for Biomarker Discovery in Voice Signal

arXiv: https://arxiv.org/abs/2110.04678 · HTML: https://mlsanity.com/paper/2110.04678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Detection of COVID-19 through the analysis of vocal fold oscillations](https://mlsanity.com/paper/2010.10707/) (2020)
- [Interpreting glottal flow dynamics for detecting COVID-19 from voice](https://mlsanity.com/paper/2010.16318/) (2020)

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

- [On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice](https://mlsanity.com/paper/2204.04802/) (2022)

## Abstract (arXiv)

This paper reflects on the effect of several categories of medical conditions on human voice, focusing on those that may be hypothesized to have effects on voice, but for which the changes themselves may be subtle enough to have eluded observation in standard analytical examinations of the voice signal. It presents three categories of techniques that can potentially uncover such elusive biomarkers and allow them to be measured and used for predictive and diagnostic purposes. These approaches include proxy techniques, model-based analytical techniques and data-driven AI techniques.