Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/13995 |
Resumo: | The morphological and dynamic characterisation of the vocal tract during speech production has been gaining greater attention due to the motivation of the latest improvements in magnetic resonance (MR) imaging; namely, with the use of higher magnetic fields, such as 3.0 Tesla. In this work, the automatic study of the vocal tract from 3.0 Tesla MR images was assessed through the application of statistical deformable models. Therefore, the primary goal focused on the analysis of the shape of the vocal tract during the articulation of European Portuguese sounds, followed by the evaluation of the results concerning the automatic segmentation, i.e. identification of the vocal tract in new MR images. In what concerns speech production, this is the first attempt to automatically characterise and reconstruct the vocal tract shape of 3.0 Tesla MR images by using deformable models; particularly, by using active and appearance shape models. The achieved results clearly evidence the adequacy and advantage of the automatic analysis of the 3.0 Tesla MR images of these deformable models in order to extract the vocal tract shape and assess the involved articulatory movements. These achievements are mostly required, for example, for a better knowledge of speech production, mainly of patients suffering from articulatory disorders, and to build enhanced speech synthesizer models. |
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Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance imagesActive and appearance shape modelsImage analysisMedical imagingModelling and segmentationMorphological studyPortuguese speech languageVocal tract shapeSpeechThe morphological and dynamic characterisation of the vocal tract during speech production has been gaining greater attention due to the motivation of the latest improvements in magnetic resonance (MR) imaging; namely, with the use of higher magnetic fields, such as 3.0 Tesla. In this work, the automatic study of the vocal tract from 3.0 Tesla MR images was assessed through the application of statistical deformable models. Therefore, the primary goal focused on the analysis of the shape of the vocal tract during the articulation of European Portuguese sounds, followed by the evaluation of the results concerning the automatic segmentation, i.e. identification of the vocal tract in new MR images. In what concerns speech production, this is the first attempt to automatically characterise and reconstruct the vocal tract shape of 3.0 Tesla MR images by using deformable models; particularly, by using active and appearance shape models. The achieved results clearly evidence the adequacy and advantage of the automatic analysis of the 3.0 Tesla MR images of these deformable models in order to extract the vocal tract shape and assess the involved articulatory movements. These achievements are mostly required, for example, for a better knowledge of speech production, mainly of patients suffering from articulatory disorders, and to build enhanced speech synthesizer models.SAGE PublicationsRepositório Científico do Instituto Politécnico do PortoVasconcelos, Maria J. M.Ventura, Sandra Moreira RuaFreitas, Diamantino R. S.Tavares, João Manuel R. S.2019-06-13T16:33:40Z20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/13995eng10.1177/0954411911431664info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:56:28Zoai:recipp.ipp.pt:10400.22/13995Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:33:50.776426Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
title |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
spellingShingle |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images Vasconcelos, Maria J. M. Active and appearance shape models Image analysis Medical imaging Modelling and segmentation Morphological study Portuguese speech language Vocal tract shape Speech |
title_short |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
title_full |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
title_fullStr |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
title_full_unstemmed |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
title_sort |
Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images |
author |
Vasconcelos, Maria J. M. |
author_facet |
Vasconcelos, Maria J. M. Ventura, Sandra Moreira Rua Freitas, Diamantino R. S. Tavares, João Manuel R. S. |
author_role |
author |
author2 |
Ventura, Sandra Moreira Rua Freitas, Diamantino R. S. Tavares, João Manuel R. S. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Vasconcelos, Maria J. M. Ventura, Sandra Moreira Rua Freitas, Diamantino R. S. Tavares, João Manuel R. S. |
dc.subject.por.fl_str_mv |
Active and appearance shape models Image analysis Medical imaging Modelling and segmentation Morphological study Portuguese speech language Vocal tract shape Speech |
topic |
Active and appearance shape models Image analysis Medical imaging Modelling and segmentation Morphological study Portuguese speech language Vocal tract shape Speech |
description |
The morphological and dynamic characterisation of the vocal tract during speech production has been gaining greater attention due to the motivation of the latest improvements in magnetic resonance (MR) imaging; namely, with the use of higher magnetic fields, such as 3.0 Tesla. In this work, the automatic study of the vocal tract from 3.0 Tesla MR images was assessed through the application of statistical deformable models. Therefore, the primary goal focused on the analysis of the shape of the vocal tract during the articulation of European Portuguese sounds, followed by the evaluation of the results concerning the automatic segmentation, i.e. identification of the vocal tract in new MR images. In what concerns speech production, this is the first attempt to automatically characterise and reconstruct the vocal tract shape of 3.0 Tesla MR images by using deformable models; particularly, by using active and appearance shape models. The achieved results clearly evidence the adequacy and advantage of the automatic analysis of the 3.0 Tesla MR images of these deformable models in order to extract the vocal tract shape and assess the involved articulatory movements. These achievements are mostly required, for example, for a better knowledge of speech production, mainly of patients suffering from articulatory disorders, and to build enhanced speech synthesizer models. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011 2011-01-01T00:00:00Z 2019-06-13T16:33:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/13995 |
url |
http://hdl.handle.net/10400.22/13995 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1177/0954411911431664 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SAGE Publications |
publisher.none.fl_str_mv |
SAGE Publications |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131430430703616 |