Electro-myographic patterns of sub-vocal Speech: Records and classification

Autores/as

  • Luis Enrique Mendoza Universidad de Pamplona
  • Jesus Peña Universidad de Pamplona
  • Jairo Lenin Ramón Valencia Universidad El Bosque

DOI:

https://doi.org/10.18270/rt.v12i2.758

Palabras clave:

Electromyography, subvocal speech, Wavelet, neuronal network

Resumen

This paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2.

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Biografía del autor/a

Luis Enrique Mendoza, Universidad de Pamplona

Research Group of GIBUP University The Pamplona. Associate Professor Program of Telecommunication engineering, Faculty of Engineering.

Jesus Peña, Universidad de Pamplona

Research Group of GIBUP University The Pamplona. Electronic Engineering of University of Pamplona, Colombia.

Jairo Lenin Ramón Valencia, Universidad El Bosque

Research Group of BIOAXIS University The Bosque. Associate Professor Program of Bioengineering, Editor of the Journal of Technology, Faculty of Engineering, PhD Industrial Technology. University Polytechnic of Cartagena, Spain. Biomedical Engineer Manuela Beltran University, Bucaramanga, Engineer Industrial Tec Specialty in Electronics, UPCT, Spain.

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Publicado

2015-12-19