Speech measures need harmonization to improve health monitoring
Towards clinical adoption of voice and speech as measures of health: the need for harmonization
Emerging TechnologiesSound
Summary
Speech and voice contain a lot of information not just about what we say, but about our health. The paper explains that differences in how speech is recorded and analyzed make it hard to use voice as a reliable health tool. The authors suggest everyone use standardized ways to measure speech features related to breathing, voice, and speaking patterns. This standardization could help turn voice analysis into a trustworthy method to track diseases like heart, lung, or brain conditions.
What this means in practice
- •For clinical data teams: Use standardized speech features to build consistent tools for monitoring neurological and respiratory health.
- •For medical device developers: Incorporate harmonized voice measurement methods into tools that detect and track cardiovascular and psychiatric diseases.$Commercial implications: Enables development of reliable commercial health monitoring devices using voice as a biomarker.
A position paper. It proposes an approach and reports no results.
Authors
Nicholas Cummins, Vikram Ramanarayanan, Daniel Low, Fabio Catania, Si-Ioi Ng, Caterina Botelho, Judith Dineley, Abir Elbeji, Julie M. Liss, Paula Andrea Perez-Toro, Visar Berisha, Thomas Quatieri
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.