An Overview of Techniques for Biomarker Discovery in Voice Signal
This work provides an overview of methods for biomarker discovery in voice signals, which could aid in diagnosing medical conditions, but it is incremental as it reviews existing techniques rather than introducing new ones.
The paper addresses the challenge of detecting subtle voice biomarkers for medical conditions that evade standard analysis, presenting three categories of techniques—proxy, model-based analytical, and data-driven AI—to uncover and measure these biomarkers for predictive and diagnostic use.
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.