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Published on: June 14, 2014
Myo-electric signals to augment speech recognition.
A D Chan1, K Englehart, B Hudgins
1Institute of Biomedical Engineering, University of New Brunswick, Fredericton, Canada. biomed@unb.ca
Medical & Biological Engineering & Computing
|August 29, 2001
Summary
Surface myo-electric signals from facial muscles show potential for improving speech recognition. This technology could enhance accuracy in noisy environments like aircraft cockpits.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Human-Computer Interaction
Background:
- Conventional speech recognition struggles in noisy environments.
- Myo-electric signals from facial muscles offer a potential complementary data source.
- Previous research has explored myo-electric signals for control, but less for speech augmentation.
Purpose of the Study:
- To investigate the presence of speech information in facial myo-electric signals.
- To assess the feasibility of using these signals to augment speech recognition systems.
- To evaluate performance in a simulated noisy environment.
Main Methods:
- Recorded five surface myo-electric signals using Ag-AgCl electrodes on a pilot oxygen mask.
- Utilized an acoustic channel for signal segmentation.
- Processed signals with wavelet transform features and classified using linear discriminant analysis.
- Tested with a ten-word vocabulary ('zero' to 'nine') in two experiments.
Main Results:
- Classification errors ranged from 0.0% to 6.1% in the first experiment (non-randomized vocabulary).
- Classification errors were 2.7% and 10.4% in the second experiment (randomized vocabulary).
- Demonstrated significant speech information content within myo-electric signals.
Conclusions:
- Surface myo-electric signals possess substantial speech information.
- There is excellent potential for using these signals to enhance conventional speech recognition.
- This approach could significantly improve system performance in acoustically challenging conditions.
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