Publicação
Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques
| Resumo: | Using acoustic analysis to classify and identify speech disorders noninvasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessingmethodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalizationmodels and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure. |
|---|---|
| Autores principais: | Fernandes, Joana Filipa Teixeira |
| Outros Autores: | Freitas, Diamantino Rui; Teixeira, João Paulo |
| Assunto: | Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173073115938816 |
|---|---|
| author | Fernandes, Joana Filipa Teixeira |
| author2 | Freitas, Diamantino Rui Teixeira, João Paulo |
| author2_role | author author |
| author_facet | Fernandes, Joana Filipa Teixeira Freitas, Diamantino Rui Teixeira, João Paulo |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Fernandes, Joana Filipa Teixeira\",\"Person.identifier.orcid\":\"0000-0002-0618-4627\"},{\"Person.name\":\"Freitas, Diamantino Rui\"},{\"Person.name\":\"Teixeira, João Paulo\",\"Person.identifier.orcid\":\"0000-0002-6679-5702\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Fernandes, Joana Filipa Teixeira Freitas, Diamantino Rui Teixeira, João Paulo |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-10-07T10:59:18Z |
| datacite.date.embargoed.fl_str_mv | 2024-10-07T10:59:18Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| datacite.titles.title.fl_str_mv | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Fernandes, Joana Filipa Teixeira Freitas, Diamantino Rui Teixeira, João Paulo |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-10-07T10:59:18Z |
| dc.date.embargoed.fl_str_mv | 2024-10-07T10:59:18Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/30321 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Nature |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| dc.title.fl_str_mv | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Using acoustic analysis to classify and identify speech disorders noninvasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessingmethodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalizationmodels and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure. |
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| eu_rights_str_mv | openAccess |
| format | conferencePaper |
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| funding.funder.alternateName_str_mv | FCT FCT FCT FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia |
| funding.identifier_str_mv | UIDB/05757/2020 UIDP/05757/2020 LA/P/0007/2020 2021.04729.BD |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10198/30321 |
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| institution | Instituto Politécnico de Bragança |
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| language | eng |
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| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/30321 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Fernandes, Joana Filipa Teixeira Fernandes, Joana Filipa Teixeira https://www.ciencia-id.pt/AE12-440A-299D AE12-440A-299D http://orcid.org/0000-0002-0618-4627 0000-0002-0618-4627 Freitas, Diamantino Rui Teixeira, João Paulo Teixeira, João Paulo https://www.ciencia-id.pt/4F15-B322-59B4 4F15-B322-59B4 http://orcid.org/0000-0002-6679-5702 0000-0002-6679-5702 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Springer Nature |
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| spelling | engSpringer Naturept_PTUsing acoustic analysis to classify and identify speech disorders noninvasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessingmethodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalizationmodels and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure.application/pdfpt_PTAccuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing TechniquesPersonalFernandes, Joana Filipa TeixeiraDSpacehttp://dspace.org/items/a6f7a119-fbc9-439f-8dd9-0bbc9ec82fadDSpacehttp://dspace.org/items/a6f7a119-fbc9-439f-8dd9-0bbc9ec82fadFernandesJoana Filipa TeixeiraCiência IDhttps://www.ciencia-id.ptAE12-440A-299DORCIDhttp://orcid.org0000-0002-0618-4627Freitas, Diamantino RuiPersonalTeixeira, João PauloDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfTeixeiraJoão PauloCiência IDhttps://www.ciencia-id.pt4F15-B322-59B4ORCIDhttp://orcid.org0000-0002-6679-5702Researcher IDhttps://www.researcherid.comN-6576-2013Scopus Author IDhttps://www.scopus.com57069567500HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-3-031-53024-1ISBNIsPartOf978-3-031-53025-8DOIIsPartOf10.1007/978-3-031-53025-8_202024-10-07T10:59:18Z20242024-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/30321http://purl.org/coar/access_right/c_abf2open accessOutliersNormalizationSpeech PathologiesSpeech FeaturesMachine LearningVocal Acoustic Analysis615241 bytesFundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Roboticsinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PTUIDB/05757/20206817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Roboticsinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PTUIDP/05757/20206817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaAssociate Laboratory for Sustainability and Tecnology in Mountain Regionsinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PTLA/P/0007/20206817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaSistema de Apoio ao Diagnóstico de Patologias Vocaisinfo:eu-repo/grantAgreement/FCT/OE/2021.04729.BD/PT2021.04729.BDOECrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_5794conference paper2024http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/ba1b6f51-be5f-46d5-ab44-c3271745974d/download287299 |
| spellingShingle | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques Fernandes, Joana Filipa Teixeira Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| status | SINGLETON |
| subject.fl_str_mv | Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| title | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| title_full | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| title_fullStr | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| title_full_unstemmed | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| title_short | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| title_sort | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques |
| topic | Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| topic_facet | Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis |
| url | http://hdl.handle.net/10198/30321 |
| visible | 1 |
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