Transformation of electromyographic signals of subvocal speech using compressive sensing and artificial intelligence

Authors

  • José Daniel Ramírez Corzo Universidad de Pamplona
  • Luis Enrique Mendoza Universidad de Pamplona
  • Leonardo Carrascal Universidad de Pamplona

DOI:

https://doi.org/10.24054/sei.v3i1.1189

Keywords:

Sparse, Python, electromyography multi - resolution analysis, Raspberry Pi

Abstract

This article shows the acquisition of electrical signals from the nerves of the throat and oral cords, transforming them into voice signals using advanced techniques in digital signal processing and artificial intelligence, based on the extraction of patterns based on compressive sensing, Entropy, discrete Wavelet transform and a classifier based on vector support machines of least squares, once the system has been calibrated, the results showed that 95% + - 0.34 of data were correctly classified. The developed system was used in 500 signals and is based on an Open Source programming language implemented in an embedded system. Finally, it was shown that it is possible to use compressive sensing to extract subvocal speech patterns.

References

G. E. A. D. y. A. O. Gutierrez J, "Interface developed for the detection of sub-vocal speech," Revista Tecnura, vol. Volumen 17, no. Numero 37, p. paginas 138 – 152, Julio - Septiembre de 2013 .

C. Jorgensen, "Sub auditory speech recognition based on EMG signals," Neural Networks, 2003. Proceedings of the International Joint Conference on, 20-24 July 2003.

O. L. Ramos Sandoval, "Arquitectura Algorítmica para el Reconocimiento de Patrones Fonéticos del Habla Sub-Vocal en el Español," Universidad Distrital Francisco José de Caldas, 28 10 2016. [Online]. Available: http://hdl.handle.net/11349/4473. [Accessed 25 06 2017].

"Web Browser Control Using EMG Based Sub Vocal Speech Recognition," System Sciences, 2005. HICSS '05. Proceedings of the 38th Annual Hawaii International Conference on, 6-6 Jan. 2005 Big Island, HI, USA, USA.

P. J. M. L. y. V. H. Mendoza L, "Speech Subvocal Signal Processing using Packet Wavelet and Neuronal Network.," TecnoLógicas, Vols. ISSN 0123-7799 Edición EspeciaL, pp. pp. 655-667, octubre de 2013.

C. D. Hardyck and L. F. Petrinovich, "Treatment of Subvocal Speech During Reading," Journal of Reading, vol. 12, no. 5, pp. pp. 361-368., 02 - 1969.

"One Channel Subvocal Speech Phrases Recognition Using Cumulative Residual Entropy and Support Vector Machines," IEEE Latin America Transactions, vol. Volume: 13, no. Issue: 7, pp. 2135 - 2143, July 2015.

J. D. &. M. L. E. Ramírez-Corzo, "Dual silent communication system development based on subvocal speech and Raspberry Pi.," Revista Facultad de Ingeniería,, vol. 25 (43), pp. 111-121, 2016.

L. Mendoza and L. M. Meriño, "COMPRESIÓN ROBUSTA USANDO COMPRESSIVE SENSING (CS)," Revista Colombiana de Tecnologías de Avanzada, pp. ISSN: 1692 - 7257 , 2009.}

Sergio S Pinto, Luis E Mendoza, Hernando J Velandia, Valentin Molina, Leonor J Cervelon. Compressive sensing hardware in 1-d signals. Revista Tecciencia. 2015.

Published

2020-01-20

How to Cite

Ramírez Corzo, J. D., Mendoza, L. E., & Carrascal, L. (2020). Transformation of electromyographic signals of subvocal speech using compressive sensing and artificial intelligence. Revista Semilleros De Investigación, 3(1), 1–9. https://doi.org/10.24054/sei.v3i1.1189

Issue

Section

Artículos