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AI Schizophrenia Diagnosis Through Speech Features F0 and MFCC

In: Health Technologies and Demographic Challenges

Author

Listed:
  • Felipe Lage Teixeira

    (Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politécnico de Bragança
    Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança
    School of Sciences and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Engineering Department
    Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro)

  • Joana Filipa Teixeira Fernandes

    (Research Centre in Digitalization and Intelligent Robotics (CEDRI), Instituto Politécnico de Bragança
    Faculty of Engineering of Porto (FEUP))

  • 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
    Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro
    University of Aveiro, Intelligent Systems Associate Laboratory (LASI))

  • 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))

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.

Suggested Citation

  • Felipe Lage Teixeira & Joana Filipa Teixeira Fernandes & Adriana Ondina Pestana Santos & J. L. Pio Abreu & Salviano Pinto Soares & João Paulo Teixeira, 2025. "AI Schizophrenia Diagnosis Through Speech Features F0 and MFCC," Springer Proceedings in Business and Economics, in: Pedro Miguel Gaspar & Juan Manuel Cueva Lovelle & Carlos Mentenegro-Marín & Teresa Guarda (ed.), Health Technologies and Demographic Challenges, pages 117-126, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-94901-2_10
    DOI: 10.1007/978-3-031-94901-2_10
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