Array-based Electromyographic Silent Speech Interface

An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study is concerned with introducing an EMG recording system based on multi-channel electrode arrays. We first present our new system and introduce a method to deal with undertraining effects which emerge due to the high dimensionality of our EMG features. Second, we show that Independent Component Analysis improves the classification accuracy of the EMG array-based recognizer by up to 22.9% relative, which is a first example of an EMG signal processing method which is specifically enabled by our new array-based system. We evaluate our system on recordings of audible speech; achieving an optimal average word error rate of 10.9% with a training set of less than 10 minutes on a vocabulary of 108 words.

[1]  John G. Webster,et al.  Driven-right-leg circuit design , 1983, IEEE Transactions on Biomedical Engineering.

[2]  António J. S. Teixeira,et al.  Towards a Silent Speech Interface for Portuguese - Surface Electromyography and the Nasality Challenge , 2012, BIOSIGNALS.

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[5]  Javier M. Antelis,et al.  Syllable-based speech recognition using EMG , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[6]  J. M. Gilbert,et al.  Silent speech interfaces , 2010, Speech Commun..

[7]  Tanja Schultz,et al.  Towards Speaker-adaptive Speech Recognition based on Surface Electromyography , 2009, BIOSIGNALS.

[8]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[9]  Geoffrey E. Hinton,et al.  Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates , 2000, J. VLSI Signal Process..

[10]  Tanja Schultz,et al.  Towards continuous speech recognition using surface electromyography , 2006, INTERSPEECH.

[11]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[12]  Sorin Dusan,et al.  Speech interfaces based upon surface electromyography , 2010, Speech Commun..

[13]  Jianhua Z. Huang,et al.  Sparse Linear Discriminant Analysis with Applications to High Dimensional Low Sample Size Data , 2009 .

[14]  L. Maier-Hein,et al.  Session independent non-audible speech recognition using surface electromyography , 2005, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005..

[15]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.