# Survey on biomarkers in human vocalizations

arXiv: https://arxiv.org/abs/2407.17505 · HTML: https://mlsanity.com/paper/2407.17505/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 136 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Predicting Tuberculosis from Real-World Cough Audio Recordings and Metadata](https://mlsanity.com/paper/2307.04842/) (2023)
- [DDKtor: Automatic Diadochokinetic Speech Analysis](https://mlsanity.com/paper/2206.14639/) (2022)

## Abstract (arXiv)

Recent years has witnessed an increase in technologies that use speech for the sensing of the health of the talker. This survey paper proposes a general taxonomy of the technologies and a broad overview of current progress and challenges. Vocal biomarkers are often secondary measures that are approximating a signal of another sensor or identifying an underlying mental, cognitive, or physiological state. Their measurement involve disturbances and uncertainties that may be considered as noise sources and the biomarkers are coarsely qualified in terms of the various sources of noise involved in their determination. While in some proposed biomarkers the error levels seem high, there are vocal biomarkers where the errors are expected to be low and thus are more likely to qualify as candidates for adoption in healthcare applications.