Dual silent communication system development based on subvocal speech and Raspberry Pi

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José Daniel Ramírez-Corzo
Luis Enrique Mendoza


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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[1] C. Jorgensen and K. Binsted, "Web Browser Control Using EMG Based Sub Vocal Speech Recognition," in Proceedings of the 38th Annual Hawaii International Conference on System Sciences, p. 294c, Jan. 2005. DOI: http://dx.doi.org/10.1109/HICSS.2005.683.

[2] J. Mendes, R. Robson, S. Labidi, and A. Barros, "Subvocal Speech Recognition Based on EMG Signal Using Independent Component Analysis and Neural Network MLP," in Congress on Image and Signal Processing, vol. 1, pp. 221-224, May. 2008. DOI: http://dx.doi.org/10.1109/cisp.2008.741.

[3] H. Curtis and L. F. Petrinovich, "Treatment of Subvo-cal Speech During Reading," Journal of Reading, vol. 12 (5), pp. 361-368, Feb. 1969.

[4] E. N. Gamma, D. Amaya, and O. L. Ramos, "Revisión de las tecnologías y aplicaciones del habla subvocal," Ingeniería, vol. 20 (2), pp. 277-288, 2015. DOI: http://dx.doi.org/10.14483/udistrital.jour.reving.2015.2.a07.

[5] J. A. Gutiérrez, E. N. Gamma, D. Amaya, and O. F. Avilés, "Desarrollo de interfaces para la detección del habla sub-vocal," Tecnura, vol. 17(37), pp. 138 - 152, Jul. 2013. DOI: http://dx.doi.org/10.14483/udistrital.jour.tecnura.2013.3.a12.

[6] R. Merletti and P. Philip A, Electromyography Physiology, Engineering, and Noninvasive Applications, 2004.

[7] J. Peña Rodriguez and L. E. Mendoza, Adquisición y procesamiento de señales electromiográficas basadas en habla subvocal, Pamplona, Dec. 2010.

[8] L. E. Mendoza, J. Peña, L. A. Muñoz-Bedoya, and H. J. Velandia-Villamizar, "Procesamiento de señales provenientes del habla subvocal usando Wavelet Packet y Redes Neuronales," Tecno. Lógicas, vol. Edición Especial 2013, pp. 655-667, Oct. 2013.

[9] I. Ishii, S. Takemoto, T. Takaki, M. Takamoto, K. Imon, and K. Hirakawa, "Real-time laryngoscopic measurements of vocal-fold vibration," in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6623-6626, Aug. 2011. DOI: http://dx.doi.org/10.1109/IEMBS.2011.6091633.

[10] G. Chau and G. Kemper, "One Channel Subvocal Speech Phrases Recognition Using Cumulative Residual Entropy and Support Vector Machines," IEEE Latin American Transactions, vol. 13 (7), pp. 2135-2143, Jul. 2015. DOI: http://dx.doi.org/10.1109/TLA.2015.7273769.

[11] C. Jorgensen, D. D. Lee, and S. Agabont, "Sub Auditory Speech Recognition Based on EMG Signals," Proceedings of the International Joint Conference on Neural Networks, vol. 4, pp. 3128-3133, Jul. 2003. DOI: http://dx.doi.org/10.1109/ijcnn.2003.1224072.

[12] L. E. Mendoza, J. Peña, and J. L. Ramón, " Electro-myographic patterns of sub-vocal Speech: Records and classification," Revista de Tecnologia, vol. 12 (2), pp. 35-41, Jul. 2013.

[13] SENIAM, [en línea]. Available: http://www.seniam.org/. [acceso: 09/02/2016].

[14] Texas Instrument, [en línea]. Available: http://www.ti.com/lit/ds/symlink/ina128.pdf. [acceso: 23/09/2015].

[15] TEXAS INSTRUMENT, [en línea]. Available: http://www.ti.com/lit/ds/symlink/ads7813.pdf. [acceso: 23/09/2015].

[16] Raspberry Pi, [en línea]. Available: https://www.raspberrypi.org/education/. [acceso: 01/01/2014].

[17] C. Sidney, R. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer, Pearson, 1997.


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