Automated Speech-Based Modeling of Item-Level Symptom Severity in Schizophrenia

Are naturalistic speech features associated with concurrent variation in psychotic symptom severity?In this cohort study of 356 patients with schizophrenia spectrum disorders in the Netherlands, with replication in 72 US patients, artificial intelligence–derived voice and text features from 938 speech recordings were associated with positive and negative symptom severity at both the subscale and item level. Symptoms such as hallucinations, suspiciousness, and blunted affect were associated with a less than 1-point error, within the range considered acceptable for human raters.These findings suggest that psychotic symptoms leave specific, quantifiable, and interpretable signatures in speech, supporting scalable, low-burden tools for real-time monitoring in psychosis.

Corresponding Author: Silvia Ciampelli, MSc, Center for Clinical Neuroscience and Cognition, University Medical Center Groningen, Petrus Campersingel 219, 9713 AP Groningen, the Netherlands (s.ciampelli@umcg.nl).

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