Abstract: This paper presents the results of our research in silent speech recognition (SSR) using Surface Electromyography (sEMG); which is the technology of recording the electric activation potentials of the human articulatory muscles by surface electrodes in order to recognize speech. Though SSR is still in the experimental stage, a number of potential applications seem evident. Persons who have undergone a laryngectomy, or older people for whom speaking requires a substantial effort, would be able to mouth (vocalize) words rather than actually pronouncing them. Our system has been trained with 30 utterances from each of the three subjects we had on a testing vocabulary of 4 phrases, and then tested for 15 new utterances that were not part of the training list. The system achieved an average of 91.11% word accuracy when using Support Vector Machine (SVM) classifier while the base language is English, and an average of 89.44% word accuracy using the Standard Arabic language.
Keywords: Surface Electromyography; Support Vector Machine; Hidden Markov Models; Silent Speech Recognition