Publicação
Transfer learning with audioSet to voice pathologies identification in continuous speech
| Resumo: | The classification of pathological diseases with the implementation of concepts of Deep Learning has been increasing considerably in recent times. Among the works developed there are good results for the classification in sustained speech with vowels, but few related works for the classification in continuous speech. This work uses the German Saarbrücken Voice Database with the phrase “Guten Morgen, wie geht es Ihnen?” to classify four classes: dysphonia, laryngitis, paralysis of vocal cords and healthy voices. Transfer learning concepts were used with the AudioSet database. Two models were developed based on Long-Short-Term-Memory and Convolutional Network for classification of extracted embeddings and comparison of the best results, using cross-validation. The final results allowed to obtaining 40% of f1-score for the four classes, 66% f1-score for Dysphonia x Healthy, 67% for Laryngitis x healthy and 80% for Paralysis x Healthy. |
|---|---|
| Autores principais: | Guedes, Victor |
| Outros Autores: | Teixeira, Felipe; Oliveira, Alessa Anjos de; Fernandes, Joana Filipa Teixeira; Silva, Letícia; Candido Junior, Arnaldo; Teixeira, João Paulo |
| Assunto: | Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| Ano: | 2019 |
| 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_ | 1867172880369844224 |
|---|---|
| author | Guedes, Victor |
| author2 | Teixeira, Felipe Oliveira, Alessa Anjos de Fernandes, Joana Filipa Teixeira Silva, Letícia Candido Junior, Arnaldo Teixeira, João Paulo |
| author2_role | author author author author author author |
| author_facet | Guedes, Victor Teixeira, Felipe Oliveira, Alessa Anjos de Fernandes, Joana Filipa Teixeira Silva, Letícia Candido Junior, Arnaldo Teixeira, João Paulo |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Guedes, Victor\"},{\"Person.name\":\"Teixeira, Felipe\"},{\"Person.name\":\"Oliveira, Alessa Anjos de\"},{\"Person.name\":\"Fernandes, Joana Filipa Teixeira\",\"Person.identifier.orcid\":\"0000-0002-0618-4627\"},{\"Person.name\":\"Silva, Letícia\",\"Person.identifier.orcid\":\"0000-0003-3812-2794\"},{\"Person.name\":\"Candido Junior, Arnaldo\"},{\"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 | Guedes, Victor Teixeira, Felipe Oliveira, Alessa Anjos de Fernandes, Joana Filipa Teixeira Silva, Letícia Candido Junior, Arnaldo Teixeira, João Paulo |
| datacite.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2020-04-23T09:46:13Z |
| datacite.date.embargoed.fl_str_mv | 2020-04-23T09:46:13Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| datacite.titles.title.fl_str_mv | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Guedes, Victor Teixeira, Felipe Oliveira, Alessa Anjos de Fernandes, Joana Filipa Teixeira Silva, Letícia Candido Junior, Arnaldo Teixeira, João Paulo |
| dc.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2020-04-23T09:46:13Z |
| dc.date.embargoed.fl_str_mv | 2020-04-23T09:46:13Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/21796 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| 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 | Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| dc.title.fl_str_mv | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | The classification of pathological diseases with the implementation of concepts of Deep Learning has been increasing considerably in recent times. Among the works developed there are good results for the classification in sustained speech with vowels, but few related works for the classification in continuous speech. This work uses the German Saarbrücken Voice Database with the phrase “Guten Morgen, wie geht es Ihnen?” to classify four classes: dysphonia, laryngitis, paralysis of vocal cords and healthy voices. Transfer learning concepts were used with the AudioSet database. Two models were developed based on Long-Short-Term-Memory and Convolutional Network for classification of extracted embeddings and comparison of the best results, using cross-validation. The final results allowed to obtaining 40% of f1-score for the four classes, 66% f1-score for Dysphonia x Healthy, 67% for Laryngitis x healthy and 80% for Paralysis x Healthy. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/4a10d607-dec6-4f19-9a9a-441b9b3cc73a/download |
| id | ipb_07bc9f09c1bb7b2d917ca011366e4cc2 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/21796 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/21796 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Guedes, Victor Teixeira, Felipe Teixeira, Felipe https://www.ciencia-id.pt/0E17-62FB-AA17 0E17-62FB-AA17 Oliveira, Alessa Anjos de 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 Silva, Letícia Silva, Letícia https://www.ciencia-id.pt/C01E-87BA-67D7 C01E-87BA-67D7 http://orcid.org/0000-0003-3812-2794 0000-0003-3812-2794 Candido Junior, Arnaldo 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 | 2019 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engElsevierpt_PTThe classification of pathological diseases with the implementation of concepts of Deep Learning has been increasing considerably in recent times. Among the works developed there are good results for the classification in sustained speech with vowels, but few related works for the classification in continuous speech. This work uses the German Saarbrücken Voice Database with the phrase “Guten Morgen, wie geht es Ihnen?” to classify four classes: dysphonia, laryngitis, paralysis of vocal cords and healthy voices. Transfer learning concepts were used with the AudioSet database. Two models were developed based on Long-Short-Term-Memory and Convolutional Network for classification of extracted embeddings and comparison of the best results, using cross-validation. The final results allowed to obtaining 40% of f1-score for the four classes, 66% f1-score for Dysphonia x Healthy, 67% for Laryngitis x healthy and 80% for Paralysis x Healthy.application/pdfpt_PTTransfer learning with audioSet to voice pathologies identification in continuous speechGuedes, VictorPersonalTeixeira, FelipeDSpacehttp://dspace.org/items/764c5209-b9ab-479e-b5be-59fbe07c784bDSpacehttp://dspace.org/items/764c5209-b9ab-479e-b5be-59fbe07c784bTeixeiraFelipeCiência IDhttps://www.ciencia-id.pt0E17-62FB-AA17Oliveira, Alessa Anjos dePersonalFernandes, 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.org0000-0002-0618-4627PersonalSilva, LetíciaDSpacehttp://dspace.org/items/a2aa1be8-574c-4e0b-afd6-d3c61efad820DSpacehttp://dspace.org/items/a2aa1be8-574c-4e0b-afd6-d3c61efad820SilvaLetíciaCiência IDhttps://www.ciencia-id.ptC01E-87BA-67D7ORCIDhttp://orcid.org0000-0003-3812-2794Candido Junior, ArnaldoPersonalTeixeira, 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.ptDOIIsPartOf10.1016/j.procs.2019.12.2332020-04-23T09:46:13Z20192019-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/21796http://purl.org/coar/access_right/c_abf2open accessLong short term memoryConvolutional neural networkSVDDeep learningVoice pathologies diagnose886038 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2019http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/4a10d607-dec6-4f19-9a9a-441b9b3cc73a/downloadInternational Conference on ENTERprise Information Systems, International Conference on Project MANagement164662669Tunisia |
| spellingShingle | Transfer learning with audioSet to voice pathologies identification in continuous speech Guedes, Victor Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| status | SINGLETON |
| subject.fl_str_mv | Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| title | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| title_full | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| title_fullStr | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| title_full_unstemmed | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| title_short | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| title_sort | Transfer learning with audioSet to voice pathologies identification in continuous speech |
| topic | Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| topic_facet | Long short term memory Convolutional neural network SVD Deep learning Voice pathologies diagnose |
| url | http://hdl.handle.net/10198/21796 |
| visible | 1 |