Spatial Artifact Detection for Multi-Channel EMG-Based Speech Recognition
by , , ,
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 high-dimensional, 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).
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PDF URL: https://www.csl.uni-bremen.de/cms/images/documents/publications/Heistermann_et_al_BS2014_SpatialArtifactDetection.pdf
Reference:
Spatial Artifact Detection for Multi-Channel EMG-Based Speech Recognition (Till Heistermann, Matthias Janke, Michael Wand, Tanja Schultz), In 7th International Conference on Bio-inspired Systems and Signal Processing, 2014. (BIOSIGNALS 2014)
Bibtex Entry:
@inproceedings{heistermann2014spatial,
  title={Spatial Artifact Detection for Multi-Channel EMG-Based Speech Recognition},
  year={2014},
  note={BIOSIGNALS 2014},
  booktitle={7th International Conference on Bio-inspired Systems and Signal Processing},
  url={https://www.csl.uni-bremen.de/cms/images/documents/publications/Heistermann_et_al_BS2014_SpatialArtifactDetection.pdf},
  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 high-dimensional, 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).},
  author={Heistermann, Till and Janke, Matthias and Wand, Michael and Schultz, Tanja}
}