Automated speech-fluency explanations for schizophrenia diagnosis.

ABSTRACT: Schizophrenia is a chronic and severe mental disorder that still relies on time-intensive, clinician-administered assessments. Although several automated approaches have been proposed to support diagnosis, these systems often lack the level of explainability necessary for informed clinical decision-making. In this study, we present a fully automated and explainable pipeline for detecting schizophrenia from audio recordings of verbal fluency tests, collected from 126 Slovene-speaking participants (68 healthy controls, 58 individuals diagnosed with schizophrenia), leveraging recent advancements in automatic speech recognition (ASR) and large language model (LLM) systems. We evaluated three ASR models-Truebar, Whisper, and Soniox-for transcription quality, and selected the best-performing sys

SUBMITTER: Rajher R 

PROVIDER: S-EPMC12830904 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature