Voiceless Arabic vowels recognition using facial EMG

Abstract

This work attempts to recognize the Arabic vowels based on facial electromyograph (EMG) signals, to be used for people with speech impairment and for human computer interface. Vowels were selected since they are the most difficult letters to recognize by people in Arabic language. Twenty subjects (7 females and 13 males) were asked to pronounce three Arabic vowels continuously in a random order. Facial EMG signals were recorded over three channels from the three main facial muscles that are responsible for speech. The EMG signals are then pre-processed to eliminate noise and interference signals. Segmentation procedure was implemented to extract the time event that corresponds to each vowel based on a moving standard deviation window. The accuracy of the segmentation procedure was found to be 94%. The recognition of the vowels was carried out by extracting features from the EMG in three domains: the temporal, the spectral, and the time frequency using the wavelet packet transform. Classification of the extracted features was then finally performed using different classification methods implemented in the WEKA software. The random forest classifier with time frequency features showed the best performance with an accuracy of 77% evaluated using a 10-fold cross-validation.

Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

“…Currently, many research studies are underway into means of enabling the disabled and elderly to communicate effectively with machine or computer. Depending on the users' capabilities, different types of interface have been proposed, such as speech recognition based on both voice (Raab et al, 2011) and surface electromyography (Fraiwan et al, 2011), lip movement control system (Shaikh et al, 2011), visionbased multiple gestures (Reale et al, 2011), sip-and-puff *Corresponding author. E-mail: angkoon.p@hotmail.com.…”

Automatic Speech Recognition in Different Languages Using High-Density Surface Electromyography Sensors

“…The language also acts as an essential factor in the speech recognition, and many different sEMG-based speech recognition systems were developed in previous studies for different languages, such as English [14,15], Chinese [16,17], Japanese [20], Portuguese [57], Spanish [22] and Arabic [58]. In this study, the performances of sEMG-based speech recognition were systemically compared between English and Chinese under different conditions.…”

“…For example, fifteen English words were classified by using the sEMG signals recorded from two sensors over the neck muscles of the subject with a firefighter's self-contained breathing apparatus [7]. In another study, a three-channel EMG system was developed for patients with speech impairment, and three Arabic vowels were recognized by using the sEMG signals recorded from facial muscles [8]. Three channels of sEMG sensors were placed on the facial muscles, and eleven voiceless Bangla vowels were classified by using the artificial neural network [9].…”

Control of a powered prosthetic device via a pinch gesture interface

“…The moving standard deviation is a method that is useful in various applications where the signal is noisy or the baseline is not stable. Its use and properties have been discussed for various biomedical applications 6,7,8 . In this study, an Arduino based statistics library 9 was utilized for part of the code.…”