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Electro-myographic patterns of sub-vocal Speech: Records and classification

Electro-myographic patterns of sub-vocal Speech: Records and classification;
Electro-myographic patterns of sub-vocal Speech: Records and classification

dc.creatorMendoza, Luis Enrique
dc.creatorPeña, Jesus
dc.creatorRamón Valencia, Jairo Lenin
dc.date2015-12-19
dc.date.accessioned2020-08-21T20:40:10Z
dc.date.available2020-08-21T20:40:10Z
dc.identifierhttps://revistas.unbosque.edu.co/index.php/RevTec/article/view/758
dc.identifier10.18270/rt.v12i2.758
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/10687
dc.descriptionThis paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2.en-US
dc.descriptionThis paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2.es-AR
dc.descriptionThis paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherUniversidad El Bosquees-ES
dc.relationhttps://revistas.unbosque.edu.co/index.php/RevTec/article/view/758/350
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dc.relation/*ref*/Marcos C. Goñi y Alejandro P. de la Hoz, “Análisis de Señales Biomédicas Mediante Transformada Wavelet”, Concurso de Trabajos Estudiantiles EST, Universidad Nacional de San Martín, Argentina, 2005.
dc.relation/*ref*/Dora María Ballesteros Larrotta, “Aplicación de la Transformada Wavelet Discreta en el Filtrado de Señales Bioeléctricas,”Umbral Científico, Fundación Universitaria Manuela Beltrán, Bogotá, Colombia, pp. 92-98, Dic. 2004.
dc.relation/*ref*/F. A. Sepúlveda, “Extracción de Parámetros de Señales de Voz usando Técnicas de Análisis en Tiempo-Frecuencia,” Universidad Nacional de Colombia, Manizales, Colombia, 2004.
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dc.sourceJournal of Technology; Vol 12 No 2 (2013): Transportes sustentables; 35-41en-US
dc.sourceRevista de Tecnología; ##issue.vol## 12 ##issue.no## 2 (2013): Transportes sustentables; 35-41es-AR
dc.sourceRevista de Tecnología; Vol. 12 Núm. 2 (2013): Transportes sustentables; 35-41es-ES
dc.source1692-1399
dc.titleElectro-myographic patterns of sub-vocal Speech: Records and classificationen-US
dc.titleElectro-myographic patterns of sub-vocal Speech: Records and classificationes-AR
dc.titleElectro-myographic patterns of sub-vocal Speech: Records and classificationes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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