Sub auditory speech recognition based on EMG signals
Jorgensen, C.; Lee, D.D.; Agabont, S.
Neural Networks, 2003. Proceedings of the International Joint Conference on
Volume 4, Issue , 20-24 July 2003 Page(s): 3128 - 3133 vol.4
Digital Object Identifier   10.1109/IJCNN.2003.1224072
Summary: Sub acoustic electromyogram (EMG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub acoustically pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

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