Quantifying Articulatory Coordination as a Biomarker for Schizophrenia

TL;DR

This paper introduces an interpretable speech analysis framework using eigenspectra and WSED scores to quantify vocal tract coordination, serving as a severity-sensitive biomarker for schizophrenia.

Contribution

It presents a novel, interpretable method that captures articulatory coordination patterns and correlates with symptom severity, improving clinical insights over existing binary diagnostic tools.

Findings

Eigenspectra plots distinguish complex from simple coordination patterns.

WSED scores reliably separate schizophrenia severity levels.

Scores correlate with positive and negative symptom balance.

Abstract

Advances in artificial intelligence (AI) and deep learning have improved diagnostic capabilities in healthcare, yet limited interpretability continues to hinder clinical adoption. Schizophrenia, a complex disorder with diverse symptoms including disorganized speech and social withdrawal, demands tools that capture symptom severity and provide clinically meaningful insights beyond binary diagnosis. Here, we present an interpretable framework that leverages articulatory speech features through eigenspectra difference plots and a weighted sum with exponential decay (WSED) to quantify vocal tract coordination. Eigenspectra plots effectively distinguished complex from simpler coordination patterns, and WSED scores reliably separated these groups, with ambiguity confined to a narrow range near zero. Importantly, WSED scores correlated not only with overall BPRS severity but also with the balance between positive and negative symptoms, reflecting more complex coordination in subjects with pronounced positive symptoms and the opposite trend for stronger negative symptoms. This approach offers a transparent, severity-sensitive biomarker for schizophrenia, advancing the potential for clinically interpretable speech-based assessment tools.