Authors:
Till Heistermann
;
Matthias Janke
;
Michael Wand
and
Tanja Schultz
Affiliation:
Karlsruhe Institute of Technology, Germany
Keyword(s):
Silent Speech Interfaces, EMG, Artifact Removal, ICA, Speech Recognition, Array Processing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cybernetics and User Interface Technologies
;
Data Manipulation
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Information and Systems Security
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Speech Recognition
;
Wearable Sensors and Systems
Abstract:
We introduce a spatial artifact detection method for a surface electromyography (EMG) based speech recognition
system. The EMG signals are recorded using grid-shaped electrode arrays affixed to the speakers
face. Continuous speech recognition is performed on the basis of these signals. As the EMG data are highdimensional,
Independent Component Analysis (ICA) can be applied to separate artifact components from the
content-bearing signal. The proposed artifact detection method classifies the ICA components by their spatial
shape, which is analyzed using the spectra of the spatial patterns of the independent components. Components
identified as artifacts can then be removed. Our artifact detection method reduces the word error rates (WER)
of the recognizer significantly. We observe a slight advantage in terms of WER over the temporal signal based
artifact detection method by (Wand et al., 2013a).