Operationalizing Clinical Speech Analytics: Moving From Features to Measures for Real-World Clinical Impact.

Area of Science:

• Speech-language pathology
• Biomedical engineering
• Clinical informatics

Background:

• Conventional supervised machine learning in clinical speech analytics faces challenges in generalizability and interpretability.
• High-dimensional speech features often lack direct clinical relevance.
• A need exists for validated speech measures that operationalize specific clinical constructs.

Purpose of the Study:

• Advocate for a methodological shift in clinical speech analytics.
• Transition from high-dimensional speech features to clinically validated speech measures.
• Enhance model generalizability and clinical applicability in real-world healthcare settings.

Main Methods:

• Propose a framework focusing on validated speech measures tied to specific speech constructs.
• Detail a case study on developing an articulatory precision measure for amyotrophic lateral sclerosis (ALS).
• Outline the process from ideation to Food and Drug Administration (FDA) breakthrough status designation.

Main Results:

• Operationalizing articulatory precision into a quantifiable measure yielded robust, clinically meaningful results.
• The validated measure demonstrated high correlation with clinical status and speech intelligibility.
• The measure's application in a clinical trial led to FDA breakthrough status designation.

Conclusions:

• Transitioning to speech measures offers a targeted approach for clinical speech analytics development.
• This shift ensures models are technically sound, clinically relevant, and interpretable.
• Encourage adoption of this approach for interpretable speech representations in clinical settings.