Argentine Spanish segmental duration prediction

Autores
Torres, H. M.; Gurlekian, J. A.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper we model the segmental duration of Spanish spoken in Buenos Aires, considering its application in a text-to-speech system. The work was performed on two hand labeled databases. We use artificial neural networks as predictor, and all the input features can be extracted automatically from the speech text. We experimented with a neural network for all phonemes and one neural network for phoneme. In both cases the results are very promising for the two databases used. The order of importance of input features revealed to be different for each of the methods tested and different according to the speaker style.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Phone duration prediction
Prosody prediction
Text-To-Speech
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/123916

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spelling Argentine Spanish segmental duration predictionTorres, H. M.Gurlekian, J. A.Ciencias InformáticasPhone duration predictionProsody predictionText-To-SpeechIn this paper we model the segmental duration of Spanish spoken in Buenos Aires, considering its application in a text-to-speech system. The work was performed on two hand labeled databases. We use artificial neural networks as predictor, and all the input features can be extracted automatically from the speech text. We experimented with a neural network for all phonemes and one neural network for phoneme. In both cases the results are very promising for the two databases used. The order of importance of input features revealed to be different for each of the methods tested and different according to the speaker style.Sociedad Argentina de Informática e Investigación Operativa2012-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf156-167http://sedici.unlp.edu.ar/handle/10915/123916enginfo:eu-repo/semantics/altIdentifier/url/https://41jaiio.sadio.org.ar/sites/default/files/14_AST_2012.pdfinfo:eu-repo/semantics/altIdentifier/issn/1850-2806info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:29:43Zoai:sedici.unlp.edu.ar:10915/123916Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:29:43.455SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Argentine Spanish segmental duration prediction
title Argentine Spanish segmental duration prediction
spellingShingle Argentine Spanish segmental duration prediction
Torres, H. M.
Ciencias Informáticas
Phone duration prediction
Prosody prediction
Text-To-Speech
title_short Argentine Spanish segmental duration prediction
title_full Argentine Spanish segmental duration prediction
title_fullStr Argentine Spanish segmental duration prediction
title_full_unstemmed Argentine Spanish segmental duration prediction
title_sort Argentine Spanish segmental duration prediction
dc.creator.none.fl_str_mv Torres, H. M.
Gurlekian, J. A.
author Torres, H. M.
author_facet Torres, H. M.
Gurlekian, J. A.
author_role author
author2 Gurlekian, J. A.
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Phone duration prediction
Prosody prediction
Text-To-Speech
topic Ciencias Informáticas
Phone duration prediction
Prosody prediction
Text-To-Speech
dc.description.none.fl_txt_mv In this paper we model the segmental duration of Spanish spoken in Buenos Aires, considering its application in a text-to-speech system. The work was performed on two hand labeled databases. We use artificial neural networks as predictor, and all the input features can be extracted automatically from the speech text. We experimented with a neural network for all phonemes and one neural network for phoneme. In both cases the results are very promising for the two databases used. The order of importance of input features revealed to be different for each of the methods tested and different according to the speaker style.
Sociedad Argentina de Informática e Investigación Operativa
description In this paper we model the segmental duration of Spanish spoken in Buenos Aires, considering its application in a text-to-speech system. The work was performed on two hand labeled databases. We use artificial neural networks as predictor, and all the input features can be extracted automatically from the speech text. We experimented with a neural network for all phonemes and one neural network for phoneme. In both cases the results are very promising for the two databases used. The order of importance of input features revealed to be different for each of the methods tested and different according to the speaker style.
publishDate 2012
dc.date.none.fl_str_mv 2012-08
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dc.language.none.fl_str_mv eng
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