Dual silent communication system development based on subvocal speech and Raspberry Pi
This paper presents a novel methodology to develop a silent dual communication based on subvocal speech. Two electronic systems were developed for people’s wireless communication. The system has 3 main stages. The first stage is the subvocal speech electromyographic signals acquisition, in charge to...
| Main Authors: | , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2016
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5304 |
| _version_ | 1801706075656814592 |
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| author | Ramírez-Corzo, José Daniel Mendoza, Luis Enrique |
| author_facet | Ramírez-Corzo, José Daniel Mendoza, Luis Enrique |
| author_sort | Ramírez-Corzo, José Daniel |
| collection | OJS |
| description | This paper presents a novel methodology to develop a silent dual communication based on subvocal speech. Two electronic systems were developed for people’s wireless communication. The system has 3 main stages. The first stage is the subvocal speech electromyographic signals acquisition, in charge to extract, condition, encode and transmit the system development. This signals were digitized and registered from the throat and sent to an embedded a raspberry pi.In this device was implemented the processing, as it is called the second stage, which besides to store, assumes conditioning, extraction and pattern classification of subvocal speech signals. Mathematical techniques were used as Entropy, Wavelet analysis, Minimal Squares and Vector Support Machines, which were applied in Python free environment program. Finally, in the last stage in charge to communicate by wireless means, were developed the two electronic systems, by using 4 signal types, to classify the words: Hello, intruder, hello how are you? and I am cold to perform the silent communication.Additionally, in this article we show the speech subvocal signals’ recording system realization. The average accuracy percentage was 72.5 %, and includes a total of 50 words by class, this is 200 signals. Finally, it demonstrated that using the Raspberry Pi it is possible to set a silent communication system, using subvocal. speech signals. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-5304 |
| institution | Revista Facultad de Ingeniería |
| language | spa |
| publishDate | 2016 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-53042022-06-15T16:21:36Z Dual silent communication system development based on subvocal speech and Raspberry Pi Desarrollo de un sistema de comunicación silenciosa dual basado en habla subvocal y Raspberry Pi Ramírez-Corzo, José Daniel Mendoza, Luis Enrique entropy Raspberry Pi silent communication SVM (Support Vector Machines) subvocal speech Wavelet comunicación silenciosa entropía habla subvocal MSV (Máquinas de Soporte Vectorial) Raspberry Pi Wavelet This paper presents a novel methodology to develop a silent dual communication based on subvocal speech. Two electronic systems were developed for people’s wireless communication. The system has 3 main stages. The first stage is the subvocal speech electromyographic signals acquisition, in charge to extract, condition, encode and transmit the system development. This signals were digitized and registered from the throat and sent to an embedded a raspberry pi.In this device was implemented the processing, as it is called the second stage, which besides to store, assumes conditioning, extraction and pattern classification of subvocal speech signals. Mathematical techniques were used as Entropy, Wavelet analysis, Minimal Squares and Vector Support Machines, which were applied in Python free environment program. Finally, in the last stage in charge to communicate by wireless means, were developed the two electronic systems, by using 4 signal types, to classify the words: Hello, intruder, hello how are you? and I am cold to perform the silent communication.Additionally, in this article we show the speech subvocal signals’ recording system realization. The average accuracy percentage was 72.5 %, and includes a total of 50 words by class, this is 200 signals. Finally, it demonstrated that using the Raspberry Pi it is possible to set a silent communication system, using subvocal. speech signals. Presenta una metodología novedosa para establecer una comunicación silenciosa dual basada en habla subvocal, para ello se desarrollaron dos sistemas electrónicos que registran las señales bioeléctricas que llegan al aparato fonador, generadas al momento de realizar el proceso de lectura silenciosa por el individuo. Estos sistemas están basados en tres etapas fundamentales, la primera es la de adquisición, encargada de extraer, acondicionar, codificar y transmitir las señales electromiográficas del habla subvocal hacia la segunda etapa, denominada de procesamiento, en esta etapa, implementada en un sistema Raspberry Pi, se desarrollaron los procesos de almacenamiento, acondicionamiento, extracción de patrones y clasificación de palabras, utilizando técnicas matemáticas como: Entropía, análisis Wavelet y Máquinas de Soporte Vectorial de Mínimos Cuadrados, implementadas bajo el entorno libre de programación Python, finalmente, la última etapa del sistema se encargó de comunicar inalámbricamente los dos sistemas electrónicos, utilizando 4 clases de señales, para clasificar las palabras hola, intruso, ¿hola cómo estás? y tengo frío.Adicionalmente, en este artículo se muestra la implementación del sistema para el registro de señales de habla subvocal. El porcentaje de acierto promedio general es de 72.5 %. Se incluyen un total de 50 palabras por clase, es decir, 200 señales. Finalmente, se pudo demostrar que usando una Raspberry Pi es posible establecer un sistema de comunicación silenciosa a partir de las señales del habla subvocal. Universidad Pedagógica y Tecnológica de Colombia 2016-09-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion investigation investigación application/pdf text/html https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5304 10.19053/01211129.v25.n43.2016.5304 Revista Facultad de Ingeniería; Vol. 25 No. 43 (2016); 111-121 Revista Facultad de Ingeniería; Vol. 25 Núm. 43 (2016); 111-121 2357-5328 0121-1129 spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5304/4431 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5304/5066 |
| spellingShingle | entropy Raspberry Pi silent communication SVM (Support Vector Machines) subvocal speech Wavelet comunicación silenciosa entropía habla subvocal MSV (Máquinas de Soporte Vectorial) Raspberry Pi Wavelet Ramírez-Corzo, José Daniel Mendoza, Luis Enrique Dual silent communication system development based on subvocal speech and Raspberry Pi |
| title | Dual silent communication system development based on subvocal speech and Raspberry Pi |
| title_alt | Desarrollo de un sistema de comunicación silenciosa dual basado en habla subvocal y Raspberry Pi |
| title_full | Dual silent communication system development based on subvocal speech and Raspberry Pi |
| title_fullStr | Dual silent communication system development based on subvocal speech and Raspberry Pi |
| title_full_unstemmed | Dual silent communication system development based on subvocal speech and Raspberry Pi |
| title_short | Dual silent communication system development based on subvocal speech and Raspberry Pi |
| title_sort | dual silent communication system development based on subvocal speech and raspberry pi |
| topic | entropy Raspberry Pi silent communication SVM (Support Vector Machines) subvocal speech Wavelet comunicación silenciosa entropía habla subvocal MSV (Máquinas de Soporte Vectorial) Raspberry Pi Wavelet |
| topic_facet | entropy Raspberry Pi silent communication SVM (Support Vector Machines) subvocal speech Wavelet comunicación silenciosa entropía habla subvocal MSV (Máquinas de Soporte Vectorial) Raspberry Pi Wavelet |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5304 |
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