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AI schizophrenia diagnosis through speech features F0 and MFCC

2025Documento de conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia
dc.contributor.authorTeixeira, Felipe
dc.contributor.authorFernandes, Joana
dc.contributor.authorSantos, Adriana
dc.contributor.authorAbreu, J.
dc.contributor.authorSoares, Salviano
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2026-05-18T16:00:45Z
dc.date.available2026-05-18T16:00:45Z
dc.date.issued2025
dc.description.abstractSchizophrenia 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.por
dc.description.sponsorshipThis work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).
dc.identifier.citationTeixeira, Felipe; Fernandes, Joana; Santos, Adriana; Abreu, J.; Soares, Salviano; Teixeira, João Paulo (2025). AI schizophrenia diagnosis through speech features F0 and MFCC. In International Conference on Demographic Transition, Health and Technologies, ICDTHT 2025. p. 117-126. ISBN 978-303194900-5. DOI: 10.1007/978-3-031-94901-2_10
dc.identifier.doi10.1007/978-3-031-94901-2_10
dc.identifier.isbn978-303194900-5
dc.identifier.urihttp://hdl.handle.net/10198/36720
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature Switzerland
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
dc.relation.ispartofSpringer Proceedings in Business and Economics
dc.relation.ispartofHealth Technologies and Demographic Challenges
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleAI schizophrenia diagnosis through speech features F0 and MFCCpor
dc.typeconference object
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage126
oaire.citation.startPage117
oaire.citation.titleInternational Conference on Demographic Transition, Health and Technologies, ICDTHT 2025
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bcce
person.familyNameTeixeira
person.familyNameTeixeira
person.givenNameFelipe
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-id0E17-62FB-AA17
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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