AI Schizophrenia Diagnosis Through Speech Features F0 and MFCC

Felipe Lage Teixeira (), Joana Filipa Teixeira Fernandes (), Adriana Ondina Pestana Santos, J. L. Pio Abreu, Salviano Pinto Soares () and João Paulo Teixeira ()
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Felipe Lage Teixeira: Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politécnico de Bragança
Joana Filipa Teixeira Fernandes: Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politécnico de Bragança
Adriana Ondina Pestana Santos: Instituto Português de Oncologia de Coimbra Francisco Gentil Martins EPE
J. L. Pio Abreu: Faculty of Medicine of the University of Coimbra
Salviano Pinto Soares: School of Sciences and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Engineering Department
João Paulo Teixeira: Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratory for Sustainability and Technology in Mountain Regions (SusTEC), Polytechnic Institute of Bragança (IPB)

A chapter in Health Technologies and Demographic Challenges, 2025, pp 117-126 from Springer

Abstract: Abstract Schizophrenia affects over 20 million people globally and is often undetected in its early stages. Speech has unique characteristics that can help identify mental illnesses, including schizophrenia, which usually manifests through slower, repetitive, or incoherent speech patterns. By extracting acoustic features like fundamental frequency (F0) and Mel Frequency Cepstral Coefficients (MFCCs) and applying machine learning, we can identify patterns that distinguish healthy individuals from those with schizophrenia. In this work, was achieved 95% accuracy to classify between schizophrenic and healthy people through speech.

Keywords: Schizophrenia; Ensemble bagged trees; Ensemble boosted trees; Narrow neural network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-94901-2_10

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DOI: 10.1007/978-3-031-94901-2_10

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