Voiceless Arabic vowels recognition using facial EMG.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 7; pp. 811 - 819 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104572687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104572687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2011 vid: 49 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104572687 NLM21409427 2011187452 10.1007/s11517-011-0751-1 NLM21409427 104572687 ppf: 811 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Voiceless Arabic vowels recognition using facial EMG. aug: au: Fraiwan L Lweesy K Al-Nemrawi A Addabass S Saifan R Fraiwan, Luay Lweesy, Khaldon Al-Nemrawi, Ayat Addabass, Sondos Saifan, Rasha affil: Biomedical Engineering Department, Jordan University of Science & Technology, PO Box 3030, Irbid 22110, Jordan sug: subj: Electromyography Methods Facial Muscles Physiology Language Speech Physiology Voice Recognition Systems Female Male Information Science Methods Signal Processing, Computer Assisted Speech Production Measurement Methods User-Computer Interface Female Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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