Automated speech-fluency explanations for schizophrenia diagnosis

Rajher, Rok (avtor), Marinković, Mila (avtor), Rus Prelog, Polona (avtor), Žabkar, Jure (avtor)

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 system for further processing. We semantically enriched the transcriptions using the generative capabilities of LLMs and extracted both verbal and non-verbal features grounded in established diagnostic criteria. We assessed the relevance of these features using a Bayesian statistical framework and trained multiple classical machine learning models for automatic classification. Our best-performing model, an Explainable Boosting Machine, achieved a classification accuracy of 0.82 and an AUC of 0.90. We further generated visual explanations for the model’s predictions, establishing the first fully automated and explainable schizophrenia detection framework developed for the Slovene language. Our approach prioritizes explainability through model-transparent outputs, while still achieving performance comparable to existing automated systems for speech-based schizophrenia detection.

• Jezik: Angleški jezik
• Ključne besede: automated schizophrenia detection, automated speech recognition, verbal fluency
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko; MF - Medicinska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 14 str.
• Številčenje: Vol. 16, art. 3243
• PID: 20.500.12556/RUL-181710
• UDK: 004.934:616.895.8
• ISSN pri članku: 2045-2322
• DOI: 10.1038/s41598-025-33129-w
• COBISS.SI-ID: 264025859
• Datum objave v RUL: 14.04.2026

Gradivo je del revije

• Naslov: Scientific reports
• Skrajšan naslov: Sci. rep.
• Založnik: Nature Publishing Group
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: avtomatsko prepoznavanje shizofrenije, avtomatska prepoznava govora, besedna tekočnost

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0209
• Naslov: Umetna inteligenca in inteligentni sistemi

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: GC-0001
• Naslov: Artificial Intelligence for Science