Med Biol Eng Comput - Voiceless Arabic vowels recognition using facial EMG.

Tópicos

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Resumo

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.

Resumo Limpo

work attempt recogn arab vowel base facial electromyograph emg signal use peopl speech impair human comput interfac vowel select sinc difficult letter recogn peopl arab languag twenti subject femal male ask pronounc three arab vowel continu random order facial emg signal record three channel three main facial muscl respons speech emg signal preprocess elimin nois interfer signal segment procedur implement extract time event correspond vowel base move standard deviat window accuraci segment procedur found recognit vowel carri extract featur emg three domain tempor spectral time frequenc use wavelet packet transform classif extract featur final perform use differ classif method implement weka softwar random forest classifi time frequenc featur show best perform accuraci evalu use fold crossvalid

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