Classification of ASR Word Hypotheses using prosodic information and resampling of training data
- Autores
- Albornoz, Enrique Marcelo; Milone, Diego Humberto; Rufiner, Hugo Leonardo; López-Cózar, R.
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In this work, we propose a novel re-sampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%.
Fil: Albornoz, Enrique Marcelo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina
Fil: López-Cózar, R.. Escuela Técnica Superior en Ingeniería Informática y de Telecomunicación. Universidad de Granada; España; - Materia
-
Resampling Corpus
Support Vector Machines
Hypotheses Classification
Automatic Speech Recognition - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/1536
Ver los metadatos del registro completo
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Classification of ASR Word Hypotheses using prosodic information and resampling of training dataAlbornoz, Enrique MarceloMilone, Diego HumbertoRufiner, Hugo LeonardoLópez-Cózar, R.Resampling CorpusSupport Vector MachinesHypotheses ClassificationAutomatic Speech Recognitionhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this work, we propose a novel re-sampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%.Fil: Albornoz, Enrique Marcelo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; ArgentinaFil: López-Cózar, R.. Escuela Técnica Superior en Ingeniería Informática y de Telecomunicación. Universidad de Granada; España;Planta Piloto de Ingeniería Química2013-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/1536Albornoz, Enrique Marcelo; Milone, Diego Humberto; Rufiner, Hugo Leonardo; López-Cózar, R.; Classification of ASR Word Hypotheses using prosodic information and resampling of training data; Planta Piloto de Ingeniería Química; Latin American Applied Research; 43; 3; 7-2013; 1-50327-07931851-8796enginfo:eu-repo/semantics/altIdentifier/url/http://fich.unl.edu.ar/sinc/sinc-publications/2013/AMRL13/sinc_AMRL13.pdfinfo:eu-repo/semantics/altIdentifier/url/http://www.laar.uns.edu.ar/indexes/artic_v4303/Vol43_03_213.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:23Zoai:ri.conicet.gov.ar:11336/1536instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:06:23.893CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
title |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
spellingShingle |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data Albornoz, Enrique Marcelo Resampling Corpus Support Vector Machines Hypotheses Classification Automatic Speech Recognition |
title_short |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
title_full |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
title_fullStr |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
title_full_unstemmed |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
title_sort |
Classification of ASR Word Hypotheses using prosodic information and resampling of training data |
dc.creator.none.fl_str_mv |
Albornoz, Enrique Marcelo Milone, Diego Humberto Rufiner, Hugo Leonardo López-Cózar, R. |
author |
Albornoz, Enrique Marcelo |
author_facet |
Albornoz, Enrique Marcelo Milone, Diego Humberto Rufiner, Hugo Leonardo López-Cózar, R. |
author_role |
author |
author2 |
Milone, Diego Humberto Rufiner, Hugo Leonardo López-Cózar, R. |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Resampling Corpus Support Vector Machines Hypotheses Classification Automatic Speech Recognition |
topic |
Resampling Corpus Support Vector Machines Hypotheses Classification Automatic Speech Recognition |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
In this work, we propose a novel re-sampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%. Fil: Albornoz, Enrique Marcelo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina Fil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina Fil: López-Cózar, R.. Escuela Técnica Superior en Ingeniería Informática y de Telecomunicación. Universidad de Granada; España; |
description |
In this work, we propose a novel re-sampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/1536 Albornoz, Enrique Marcelo; Milone, Diego Humberto; Rufiner, Hugo Leonardo; López-Cózar, R.; Classification of ASR Word Hypotheses using prosodic information and resampling of training data; Planta Piloto de Ingeniería Química; Latin American Applied Research; 43; 3; 7-2013; 1-5 0327-0793 1851-8796 |
url |
http://hdl.handle.net/11336/1536 |
identifier_str_mv |
Albornoz, Enrique Marcelo; Milone, Diego Humberto; Rufiner, Hugo Leonardo; López-Cózar, R.; Classification of ASR Word Hypotheses using prosodic information and resampling of training data; Planta Piloto de Ingeniería Química; Latin American Applied Research; 43; 3; 7-2013; 1-5 0327-0793 1851-8796 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://fich.unl.edu.ar/sinc/sinc-publications/2013/AMRL13/sinc_AMRL13.pdf info:eu-repo/semantics/altIdentifier/url/http://www.laar.uns.edu.ar/indexes/artic_v4303/Vol43_03_213.pdf |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Planta Piloto de Ingeniería Química |
publisher.none.fl_str_mv |
Planta Piloto de Ingeniería Química |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1842269956597612544 |
score |
13.13397 |