Abstract
This paper investigates how feature selection influences the accuracy of vowel recognition based on surface electromyography (sEMG) derived with an electrode grid, which consists of densely-spaced multielectrodes. In previous studies on sEMG-based automatic speech recognition (sEMG-ASR), disc electrodes or parallel bar electrodes were used and located empirically. But, in this study, to avoid missing out information about speech, an electrode grid was used to measure sEMG from the submental region during the production of five Japanese vowels. For feature selection, we applied sparse discriminant analysis (SDA) to the obtained data which can include some redundant signals. It was illustrated that feature selection compressing to one tenth or one twentieth of the total features could be achieved without steep decline in recognition accuracies. Combination of dense measurement based on the electrode grid and feature selection based on SDA is an effective approach for researches on sEMG-ASR.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Denby, B., Schultz, T., Honda, K., Hueber, T., Gilbert, J.M., Brumberg, J.S.: Silent Speech Interfaces. Speech Communication 52(4), 270–287 (2010)
Chan, A.D.C., Englehart, K.B., Hudgins, B., Lovely, D.F.: Multiexpert Automatic Speech Recognition Using Acoustic and Myoelectric Signals. IEEE Trans. Biomed. Eng. 53(4), 676–685 (2006)
Deng, Y., Patel, R., Heaton, J.T., Colby, G., Gilmore, L.D., Cabrera, J., Roy, S.H., De Luca, C.J., Meltzner, G.S.: Disordered Speech Recognition Using Acoustic and sEMG Signals. In: INTERSPEECH 2009, pp. 644–647. ISCA, Brighton (2009)
Jou, S.C., Schultz, T., Walliczek, M., Kraft, F., Waibel, A.: Towards Continuous Speech Recognition Using Surface Electromyography. In: INTERSPEECH 2006, pp. 573–576. ISCA, Pittsburgh (2006)
Jou, S.C., Maier-Hein, L., Schultz, T., Waibel, A.: Articulatory Feature Classification Using Surface Electromyography. In: ICASSP, vol. 1, pp. 605–608. IEEE Press, Toulouse (2006)
Lee, K.S.: SNR-Adaptive Stream Weighting for Audio-MES ASR. IEEE Trans. Biomed. Eng. 55(8), 2001–2010 (2008)
Schultz, T., Wand, M.: Modeling Coarticulation in EMG-Based Continuous Speech Recognition. Speech Communication 52(4), 341–353 (2010)
Lapatki, B.G., Oostenveld, R., Van Dijk, J.P., Jonas, I.E., Zwarts, M.J., Stegeman, D.F.: Optimal Placement of Bipolar Surface EMG Electrodes in the Face Based on Single Motor Unit Analysis. Psychophysiology 47(2), 299–314 (2010)
Clemmensen, L.: http://www2.imm.dtu.dk/~lhc/index.html
Clemmensen, L., Hastie, T., Ersboell, K.: Sparse Discriminant Analysis. Technical report, IMM, Technical University of Denmark (2008)
Hattroi, T., Sato, T., Minato, K., Nakamura, H., Yoshida, M.: An Identification Method of Motor Units Using a 3D Template from Grid Surface Electromyography. Trans. Jpn. Soc. Med. Biol. Eng. 46(2), 268–274 (2008)
Zou, H., Hastie, T.: Regularization and Variable Selection via the Elastic Net. J. R. Stat. Soc. Series B Stat. Methodol. 67(2), 301–320 (2005)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. R. Stat. Soc. Series B Stat. Methodol. 39(1), 1–38 (1977)
Hidden Markov Model (HMM) Toolbox for Matlab, http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kubo, T., Yoshida, M., Hattori, T., Ikeda, K. (2012). Feature Selection for Vowel Recognition Based on Surface Electromyography Derived with Multichannel Electrode Grid. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-31919-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
eBook Packages: Computer ScienceComputer Science (R0)