This paper presents results of electromyography (EMG) speech recognition which captures the electric potentials that are generated by the human articulatory muscles. EMG speech recognition holds promise for mitigating the effects of high acoustic noise on speech intelligibility in communication systems. Few words have been collected from EMG from a male subject, speaking normally and sub vocally. The collected signals are then required to be filtered and transformed into features using Wavelet Packet and statistical windowing techniques. Finally, the concept of neural network with back propagation method has been used for classification of data. Using windowed signals and the trained neural network an arduino operated bot was controlled as an application to demonstrate the future scope of the paper. The success rate was 73%.

1. Chuck Jorgensen, Diana D. Lee and Shane Agabon, "Sub Auditory Speech Recognition Based on EMG Signals,"Proceedingsof the International Joint Conference on Neural Networks (IJCNN), IEEE, vol. 4, 2003, pp. 3128–3133.
2. Chuck Jorgensen and Kim Binsted, "Web Browser
3. Control Using EMG Based Sub vocal Speech
4. Recognition, "Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS), IEEE, 2005, pp. 294c. 1–294c. 8.
5. Luis Enrique Mendoza, Jesús Peña Rodríguez, Jairo Lenin Ramón Valencia. "Electro-myographic patterns of sub-vocal Speech: Records and classification" Research Group of GIBUP University The Pamplona, Colombia. November 29, 2013.
6. RatnakarMadan, Prof. Sunil Kr. Singh, and NitishaJain," Signal Filtering Using Discrete Wavelet Transform", published in International Journal of Recent Trends in Engineering, Vol 2, No. 3, November 2009.
7. Mark C. Goñi and Alexander P. de la Hoz, "Analysis of Biomedical Signals Using Wavelet Transform " ContestStudent Jobs EST , National University of San Martin,Argentina , 2005.
8. Dora María Ballesteros Larrotta, "Application of Discrete Wavelet Transform Filtering bioelectric signals," Threshold Scientific, Manuela Beltran University Foundation, Bogotá, Colombia, pp. 92-98 ,Dic. 2004.
9. Bradley J. Betts, Charles Jorgensen, "Small Vocabulary Recognition Using Surface Electromyography in an Acoustically Harsh Environment", National Aeronautics and Space Administration (NASA), Ames Research Center Moffett Field, California, 94035-1000, November 2005.
10. FA Sepulveda, "Extraction of Speech Signals Parameters techniques using Time-Frequency Analysis , " National University of Colombia , Manizales, Colombia , 2004.
11. Muhammad Zahak Jamal "Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis". Intech 2012.
12. A. Davis, S. Nordholm, and R. Togneri, "Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold," IEEE Transactionson Speech and Audio Processing, to appear, pp. 1–13.
13. K. Li, M. N. S. Swamy, and M. O. Ahmad, "An improved voice activity detection using higher order statistics," IEEE Transactions on Speech and Audio Processing,vol. 13, no. 5, 2005, pp. 965–974.
14. . J. Ramírez et al. , "Statistical voice activity detection using a multiple observation likelihood ratio test," IEEESignal Processing Letters, vol. 12, no. 10, 2005, pp. 689–692.

Emg Sub Vocal Speech Neural Network Electromyography