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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.

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AI Schizophrenia Diagnosis Through Speech Features F0 and MFCC

  • 会议论文

  • 发表日期：

    2025年

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  • Felipe Lage Teixeira,
  • Joana Filipa Teixeira Fernandes,
  • Adriana Ondina Pestana Santos,
  • J. L. Pio Abreu,
  • Salviano Pinto Soares,
  • João Paulo Teixeira

摘要

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.