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dc.contributor.author
Schlotthauer, Gaston
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dc.contributor.author
Torres, Maria Eugenia
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dc.contributor.author
Jackson Menaldi, María Cristina
dc.date.available
2023-02-27T10:50:15Z
dc.date.issued
2010-05
dc.identifier.citation
Schlotthauer, Gaston; Torres, Maria Eugenia; Jackson Menaldi, María Cristina; A Pattern Recognition Approach to Spasmodic Dysphonia and Muscle Tension Dysphonia Automatic Classification; Mosby-Elsevier; Journal Of Voice : Official Journal Of The Voice Foundation.; 24; 3; 5-2010; 346-353
dc.identifier.issn
0892-1997
dc.identifier.uri
http://hdl.handle.net/11336/188891
dc.description.abstract
Spasmodic dysphonia (SD) and muscle tension dysphonia (MTD) are two voice disorders that present similar characteristics. Usually, they can be differentiated only by experienced voice clinicians. There are many reasons that support the idea that SD is a neurological disease, requiring surgical treatments or, more usually, laryngeal botulinum toxin A injections as a therapeutic option. On the other hand, MTD is a functional disorder correctable with voice therapy. The importance of a correct diagnosis of these two disorders is critical at the treatment-selection moment. In this article, we present and compare the results of neural network and support vector machine-based methods that can help the clinicians to confirm their diagnosis. As a preliminary approach to the problem, we used only a sustained vowel /a/ to extract eight acoustic parameters. Then, a pattern recognition algorithm classifies the voice as normal, SD, or MTD. For comparison with previous works, we also separated the voices into normal and pathological (SD and MTD) voices with the methods proposed here. The results overcome the best classification rates between normal and pathological voices that have been previously reported, and demonstrate that our methods are very effective in distinguishing between MTD and SD.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Mosby-Elsevier
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
MUSCLE TENSION DYSPHONIA
dc.subject
NEURAL NETWORKS
dc.subject
SPASMODIC DYSPHONIA
dc.subject
SUPPORT VECTOR MACHINES
dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
A Pattern Recognition Approach to Spasmodic Dysphonia and Muscle Tension Dysphonia Automatic Classification
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-02-16T13:30:04Z
dc.journal.volume
24
dc.journal.number
3
dc.journal.pagination
346-353
dc.journal.pais
Países Bajos
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dc.description.fil
Fil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Torres, Maria Eugenia. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Jackson Menaldi, María Cristina. Wayne State University (wayne State University); Estados Unidos
dc.journal.title
Journal Of Voice : Official Journal Of The Voice Foundation.
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0892199708001719
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jvoice.2008.10.007
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