Towards clinical adoption of voice and speech as measures of health: the need for harmonization

2026-09-23

AI Summary

This study addresses the limited reproducibility of vocal biomarkers caused by heterogeneous data and processing pipelines, which severely impedes their clinical translation. By systematically reviewing the full lifecycle of voice-based health measurement, this work proposes a standardization pathway anchored in unified acoustic metric definitions. Specifically, it establishes a minimal core metric set encompassing respiratory and phonatory dimensions with clearly defined physiological mappings, while integrating acoustic feature computation, machine learning modeling, and clinical interpretation into a cohesive framework. The research provides reproducible metric definitions alongside practical implementation schemes, thereby laying a foundational framework for the standardization and clinical deployment of digital vocal biomarkers.

Abstract

Speech and voice are multidimensional signals that capture both communicative intent and underlying physiological processes, providing a unique, non-invasive window into health. Analyzing these signals has the potential to yield digital biomarkers that (i) provide scalable, objective measurement tools for research and clinical care and (ii) reflect the presence or progression of diverse conditions, including neurological, psychiatric, respiratory, and cardiovascular disorders. Realizing this promise, however, requires the field to overcome pervasive reproducibility and generalizability issues due to heterogeneous data collection, processing, and analysis practices. A major source of this heterogeneity is how underlying acoustic measures themselves are defined and computed. In this paper, we outline key considerations across the speech biomarker discovery lifecycle, from data collection through machine learning modeling to clinical interpretation, needed to achieve reliable, reproducible, and clinically translatable results. Chief among these is the need for harmonization efforts to start from common, precisely specified measure definitions. As a first step, we therefore provide definitions, physiological correlates, and computational implementations for a minimal, clinically interpretable set of core speech measures spanning respiration, phonation, articulation, and fluency. We close by discussing ongoing standardization efforts and the open challenges that remain in advancing the adoption of speech- and voice-based digital biomarkers.

Problem

• digital biomarkers
• speech and voice
• harmonization
• reproducibility
• clinical adoption

Innovation

• digital biomarkers
• speech harmonization
• acoustic measures
• machine learning
• clinical translation