Int. J. Medical Engineering and Informatics, Vol. 3, No. 4, 2011 311 Copyright © 2011 Inderscience Enterprises Ltd. Reliability and variability in facial electromyography for identification of speech and for human computer control: an experimental study Sridhar Poosapadi Arjunan* School of Electrical Engineering, RMIT University, GPO Box 2476V, Melbourne, Victoria 3001, Australia E-mail: sridhar.arjunan@rmit.edu.au *Corresponding author Hans Weghorn Mechatronics Department, Baden-Wurttemberg – University of Cooperative Education, Jägerstrasse 58, D-70174 Stuttgart, Germany E-mail: weghorn@dhbw-stuttgart.de Dinesh Kant Kumar School of Electrical Engineering, RMIT University, GPO Box 2476V, Melbourne, Victoria 3001, Australia E-mail: dinesh@rmit.edu.au Abstract: The need for developing reliable and flexible human computer interface is increased and applications of HCI have been in each and every field. Human factors play important role in these kinds of interfaces. This research investigates the use of facial muscle activity for a reliable interface to identify voiceless speech-based commands without any audio signals. We propose a method of measuring the relative activity of the articulatory muscles of the face for recognition of unvoiced vowels. System performance and reliability were also tested for the case of variations like inter-subject, inter-day, and different languages. In these investigations, English vowels and German vowels were used as recognition variables. The designed methodology used linear and non-linear classification based on statistical clustering techniques and artificial neural network architecture. The results show that there is a variability in facial muscle activation during vowel utterance between different subjects, different days. These results will be helpful in use of facial electromyography for identification of speech and in other application such as human computer control. Keywords: human factors; reliability; speech recognition; human-computer interface; facial movement; surface electromyography; EMG; bio-signal processing.