Repository logo
 
Publication

Schizophrenia diagnosis support with spectral and cepstral features of speech

2026Conference paper Restricted access
Simple item page
datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorTeixeira, Felipe
dc.contributor.authorMendes, João
dc.contributor.authorSoares,Salviano F. P.
dc.contributor.authorAbreu, J. L. Pio
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2025-12-12T11:56:38Z
dc.date.available2025-12-12T11:56:38Z
dc.date.issued2026
dc.description.abstractSchizophrenia is a severe mental illness affecting over 20 million people worldwide, significantly impairing quality of life and daily functioning. Current diagnostic methods rely heavily on subjective assessments and interactions between doctors and patients, leaving room for potential misdiagnoses. Recent advancements in technology have introduced non-invasive, fast, and user-friendly approaches, such as machine learning, to support psychiatric diagnosis. In this study, spectral features extracted from speech samples of individuals with and without schizophrenia were analyzed. Using an ensemble bagged tree model, we achieved an accuracy of 96.3%, a sensitivity of 94.6%, and an F1-score of 95.4%. These results highlight the potential of speech-based machine learning models as effective tools for aiding schizophrenia diagnosis.eng
dc.description.sponsorshipThis work was partially funded by FCT - Fundação para a Ciência e a Tecnologia (FCT) I.P., through national funds, within the scope of the UIDB/00127/2020 project (IEETA/UA, http://www.ieeta.pt/) The authors are grateful for the European Regional Development Fund (ERDF) via the Regional Operational Program North 2020; Foundation for Science and Technology (FCT, Portugal) support from national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The authors are grateful for the IN_GENIOS_DUERO_DOURO, Cooperación para la puesta en valor de la cultura industrial del Duero-Douro a través del turismo, 0247_IN_GENIOS_DUERO_DOURO_2_E, financed by FEDER, through the POCTEP. The authors are also grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC): UNIAG, UIDB/04752/2020 (DOI 10.54499/UIDB/04752/2020) and UIDP/04752/2020 (DOI 10.54499/UIDP/04752/2020). The authors would also like to thank Adriana Santos for her collaboration throughout the work.
dc.identifier.citationTeixeira, Felipe; Mendes, João; Soares,Salviano F. P.; Abreu, J. L. Pio; Teixeira, João Paulo (2026). Schizophrenia diagnosis support with spectral and cepstral features of speech. In Optimization, Learning Algorithms and Applications: 5th International Conference - Part 1. Cham: Springer Nature. p. 175–186. ISBN 9783032001368
dc.identifier.doi10.1007/978-3-032-00137-5_12
dc.identifier.isbn9783032001368
dc.identifier.isbn9783032001375
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10198/35211
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationInstitute of Electronics and Informatics Engineering of Aveiro
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationNEXTGENmOCp: Next generation of a microfluidic Organ-on-a-Chip platform (mOCp) to assess and diagnosis therapeutic effects of innovative nanomedicines
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relationApplied Management Research Unit
dc.relationApplied Management Research Unit
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSchizophrenia
dc.subjectSpeech features
dc.subjectMachine Learning
dc.subjectEnsemble bagged tree model
dc.titleSchizophrenia diagnosis support with spectral and cepstral features of speecheng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleInstitute of Electronics and Informatics Engineering of Aveiro
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleNEXTGENmOCp: Next generation of a microfluidic Organ-on-a-Chip platform (mOCp) to assess and diagnosis therapeutic effects of innovative nanomedicines
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardTitleApplied Management Research Unit
oaire.awardTitleApplied Management Research Unit
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00127%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND4ed/2021.00027.CEECIND%2FCP1664%2FCT0007/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04752%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04752%2F2020/PT
oaire.citation.conferenceDate2026
oaire.citation.endPage186
oaire.citation.startPage175
oaire.citation.titleOptimization, Learning Algorithms and Applications: 5th International Conference - Part 1
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamCEEC IND4ed
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameTeixeira
person.familyNameMendes
person.familyNameTeixeira
person.givenNameFelipe
person.givenNameJoão
person.givenNameJoão Paulo
person.identifier2726655
person.identifier663194
person.identifier.ciencia-id0E17-62FB-AA17
person.identifier.ciencia-idEA1F-844D-6BA9
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id57069567500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication764c5209-b9ab-479e-b5be-59fbe07c784b
relation.isAuthorOfPublicationb5c9de22-cf9e-47b8-b7a4-26e08fb12b28
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscoveryb5c9de22-cf9e-47b8-b7a4-26e08fb12b28
relation.isProjectOfPublicationaf5198a3-6377-4245-a289-6656629b8bfa
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublicationd0a17270-80a8-4985-9644-a04c2a9f2dff
relation.isProjectOfPublication4669ae00-038b-4dc0-9d76-8cf5f4c11de2
relation.isProjectOfPublication6255046e-bc79-4b82-8884-8b52074b4384
relation.isProjectOfPublicationdb81737a-fcb8-4cd9-b025-f7465790c52b
relation.isProjectOfPublicationfddb1487-0635-4996-8ea1-acac8476ca6b
relation.isProjectOfPublication.latestForDiscoveryaf5198a3-6377-4245-a289-6656629b8bfa

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Schizophrenia Diagnosis.pdf
Size:
2.32 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus