# Speech as a Biomarker for Disease Detection

arXiv: https://arxiv.org/abs/2409.10230 · HTML: https://mlsanity.com/paper/2409.10230/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers](https://mlsanity.com/paper/2505.18722/) (2025)
- [Evaluating the Effectiveness of Pre-Trained Audio Embeddings for Classification of Parkinson's Disease Speech Data](https://mlsanity.com/paper/2506.02078/) (2025)
- [RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification](https://mlsanity.com/paper/2507.03594/) (2025)
- [Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity](https://mlsanity.com/paper/2511.07011/) (2025)
- [A Benchmark for Early-stage Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2605.14066/) (2026)
- [Cross-Lingual Parkinson's Disease Severity Assessment Using Pre-trained Speech Embeddings: A Multi-Class Evaluation](https://mlsanity.com/paper/2609.20875/) (2026)

## Abstract (arXiv)

Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients' lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines "reference speech" and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer's and Parkinson's disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.