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Facial Muscle Activity Patterns for Recognition of Utterances in Native and Foreign Language: Testing for its Reliability and Flexibility

Facial Muscle Activity Patterns for Recognition of Utterances in Native and Foreign Language: Testing for its Reliability and Flexibility
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Author(s): Sridhar Arjunan (RMIT University, Australia), Dinesh Kant Kumar (RMIT University, Australia), Hans Weghorn (Baden-Wuerttemberg Cooperative State University, Germany)and Ganesh Naik (RMIT University, Australia)
Copyright: 2014
Pages: 19
Source title: Assistive Technologies: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-4422-9.ch076

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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 an important role in these kinds of interfaces. Research and development of new human computer interaction (HCI) techniques that enhance the flexibility and reliability for the user are important. Research on new methods of computer control has focused on three types of body functions: speech, bioelectrical activity, and use of mechanical sensors. Speech operated systems have the advantage that these provide the user with flexibility. Such systems have the potential for making computer control effortless and natural. This chapter summarizes research conducted to investigate the use of facial muscle activity for a reliable interface to identify voiceless speech based commands without any audio signals. System performance and reliability have been tested to study inter-subject and inter-day variations and impact of the native language of the speaker. The experimental results indicate that such a system has high degree of inter-subject and inter-day variations. The results also indicate that the variations of the style of speaking in the native language are low but are high when the speaker speaks in a foreign language. The results also indicate that such a system is suitable for a very small vocabulary. The authors suggest that facial sEMG based speech recognition systems may only find limited applications.

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