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 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.

• 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
• License: http://purl.org/coar/access_right/c_abf162