Vowel recognition of English and German language using Facial movement(SEMG) for Speech control based HCI

This paper examines the use of facial muscle activity (Surface Electromyogram) to recognise speech based commands in English and German language without any audio signals. The system is designed for applications based on speech control for Human Computer Interaction (HCI).The paper presents an effective technique that uses the facial muscle activity of the articulatory muscles and human factors for recognition. The difference in the speed and style of speaking varies between experiments, and this variation appears to be more pronounced when people are speaking a foreign language. To overcome this difficulty, the paper reports measuring the relative activity of the articulatory muscles for recognition of unvoiced vowels of English and German languages. In these investigations, three English vowels and three German vowels were used as recognition variables. The moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles is used to segment the signal and to identify the start and end of a silently spoken utterance. The relative muscle activity is computed by integrating and normalising the RMS values of the signals between the detected start and end markers. The output vector of this is classified using a back propagation neural network to identify the voiceless speech. The data is also tested using K means clustering technique to determine the linearity of separation of the data. The experimental results show that this technique gives high recognition rate when used for each of the participants for both of the languages. The investigations also show that the system is easy to train for a new user . The visual inspection of the plot of the experimental data suggests the formation of clusters. The results suggest that such a system is reliable for simple vowel based commands for human computer interface when it is trained for the user,who can speak one or more languages and for the people who have speech disability.