Publicação
AI schizophrenia diagnosis through speech features F0 and MFCC
| Resumo: | 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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| Autores principais: | Teixeira, Felipe |
| Outros Autores: | Fernandes, Joana; Santos, Adriana; Abreu, J.; Soares, Salviano; Teixeira, João Paulo |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | 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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