From "invisible" to "audible": Features extracted during simple speech tasks classify patient-reported fatigue in multiple sclerosis.

ABSTRACT:

Background

Fatigue is a major "invisible" symptom in people with multiple sclerosis (PwMS), which may affect speech. Automated speech analysis is an objective, rapid tool to capture digital speech biomarkers linked to functional outcomes.

Objective

To use automated speech analysis to assess multiple sclerosis (MS) fatigue metrics.

Methods

Eighty-four PwMS completed scripted and spontaneous speech tasks; fatigue was assessed with Modified Fatigue Impact Scale (MFIS). Speech was processed using an automated speech analysis pipeline (ki elements: SIGMA speech processing library) to transcribe speech and extract features. Regression models assessed associations between speech features and fatigue and validated in a separate set of 30 participants.

Results

Cohort ch

SUBMITTER: Nylander A 

PROVIDER: S-EPMC11789430 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature