Voiceless Arabic vowels recognition using facial EMG.
Luay Fraiwan1, Khaldon Lweesy, Ayat Al-Nemrawi
1Biomedical Engineering Department, Jordan University of Science & Technology, PO Box 3030, Irbid 22110, Jordan. fraiwan@just.edu.jo
Medical & Biological Engineering & Computing
|March 17, 2011
Summary
This study recognizes Arabic vowels using facial electromyography (EMG) signals for individuals with speech impairments. The random forest classifier achieved 77% accuracy, improving human-computer interfaces.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Human-Computer Interaction
Background:
- Speech impairment affects communication for many individuals.
- Facial electromyography (EMG) offers a non-invasive method for capturing speech-related muscle activity.
- Arabic vowels are phonetically complex and challenging for automated recognition.
Purpose of the Study:
- To develop a system for recognizing Arabic vowels using facial EMG signals.
- To enhance communication tools for people with speech impairments.
- To advance human-computer interface (HCI) technologies.
Main Methods:
- Recorded facial EMG signals from 20 subjects pronouncing Arabic vowels.
- Pre-processed EMG signals to remove noise and applied segmentation with 94% accuracy.
- Extracted temporal, spectral, and time-frequency features using wavelet packet transform.
Main Results:
- The random forest classifier with time-frequency features achieved the highest recognition accuracy of 77%.
- The segmentation procedure demonstrated high accuracy in isolating vowel events.
- Feature extraction across multiple domains provided comprehensive signal characteristics.
Conclusions:
- Facial EMG signals can be effectively used for recognizing Arabic vowels.
- This approach shows promise for assistive communication technologies and HCI applications.
- Further research can optimize feature extraction and classification for improved performance.
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