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...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/14159
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ingenieria/article/view/5304
https://repositorio.uptc.edu.co/handle/001/14159
- Palabra clave:
- 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
- Rights
- License
- http://purl.org/coar/access_right/c_abf162
| Summary: | 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. |
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