An Overview of Techniques for Biomarker Discovery in Voice Signal

TL;DR

This paper reviews various analytical techniques—proxy, model-based, and AI—for discovering subtle voice biomarkers linked to medical conditions, aiming to improve diagnostic and predictive capabilities.

Contribution

It categorizes and discusses three innovative approaches for detecting elusive voice biomarkers that are difficult to observe with standard methods.

Findings

Identifies three main categories of techniques for biomarker discovery.

Highlights the potential of AI in uncovering subtle voice changes.

Provides a framework for future research in voice-based diagnostics.

Abstract

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.