Sub Auditory Speech Recognition based on EMG/EPG Signals

-Sub-vocal electromyogram/electro palatogram (EMG/EPG) 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-vocally 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. Index Terrns--EMG, Sub Acoustic Speech, Wavelet, Neural Network

[1]  A. R. Lurii︠a︡,et al.  Basic Problems of Neurolinguistics , 1976 .

[2]  Glenn E. Prescott,et al.  Wavelet Transform Speech Recognition Using Vector Quantization, Dynamic Time Warping And Artificial , 1994 .

[3]  Minyue Fu,et al.  The use of wavelet transforms in phoneme recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[4]  Nick Kingsbury,et al.  The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters , 1998 .

[5]  P. A. Parker,et al.  Improving myoelectric signal classification using wavelet packets and principal components analysis , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[6]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[7]  Peter Norvig,et al.  Bioelectric Control of a 757 Class High Fidelity Aircraft Simulation , 2000 .

[8]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  B. Hudgins,et al.  Hidden Markov model classification of myoelectric signals in speech , 2001, IEEE Engineering in Medicine and Biology Magazine.

[10]  D. F. Lovely,et al.  Myo-electric signals to augment speech recognition , 2001, Medical and Biological Engineering and Computing.