Speech-Based Prioritization for Schizophrenia Intervention

TL;DR

This paper presents a speech-based AI model that ranks schizophrenia symptom severity, enabling scalable, remote, and continuous monitoring to improve clinical triage and resource allocation.

Contribution

It introduces a novel pairwise comparison approach using speech features and the Bradley-Terry model for severity ranking, outperforming previous regression methods.

Findings

Outperforms previous regression-based models on ranking metrics

Enables automated, remote monitoring of schizophrenia severity

Facilitates better clinical triage and prioritization

Abstract

Millions of people suffer from mental health conditions, yet many remain undiagnosed or receive delayed care due to limited clinical resources and labor-intensive assessment methods. While most machine-assisted approaches focus on diagnostic classification, estimating symptom severity is essential for prioritizing care, particularly in resource-constrained settings. Speech-based AI provides a scalable alternative by enabling automated, continuous, and remote monitoring, reducing reliance on subjective self-reports and time-consuming evaluations. In this paper, we introduce a speech-based model for pairwise comparison of schizophrenia symptom severity, leveraging articulatory and acoustic features. These comparisons are used to generate severity rankings via the Bradley-Terry model. Our approach outperforms previous regression-based models on ranking-based metrics, offering a more effective solution for clinical triage and prioritization.