By João Freitas, António Teixeira, Miguel Sales Dias, Samuel Silva
This publication offers a large and finished evaluate of the prevailing technical ways within the sector of silent speech interfaces (SSI), either in thought and in software. every one method is defined within the context of the human speech creation procedure, permitting the reader to obviously comprehend the rules in the back of SSI normally and throughout assorted tools. also, the e-book explores the mixed use of other information assets, accrued from numerous sensors, so one can take on the constraints of easier SSI methods, addressing present demanding situations of this box. The ebook additionally offers information regarding current SSI purposes, assets and an easy educational on the right way to construct an SSI.
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Additional resources for An Introduction to Silent Speech Interfaces
2009) studied the phonetic discrimination in the signal. In this study, the authors tried to determine a set of natural sub-word units, concluding that the most prominent groupings of consonants include both place and manner of articulation classes and that, for vowels, the most salient groups included close, open, and round vowels. In 2010, Srinivasan et al. (2010) were able to improve upon previous results and achieved an overall accuracy of 33 %, also on a continuous digit recognition task.
5946514 Wand M, Janke M, Schultz T (2011) Investigations on speaking mode discrepancies in EMGbased speech recognition. Interspeech 2011:601–604 Wand M, Janke M, Schultz T (2012) Decision-tree based analysis of speaking mode discrepancies in EMG-based speech recognition. In: International conference on bio-inspired systems and signal processing (BIOSIGNALS 2012), pp 101–109 Wand M, Schulte C, Janke M, Schultz T (2013) Array-based electromyographic silent speech interface. In: International conference on bio-inspired systems and signal processing (BIOSIGNALS 2013), pp 89–96 Wand M, Janke M, Schultz T (2014) (2014) The EMG-UKA corpus for electromyographic speech processing.
These patterns can be measured via a bioelectric technique based on different types of sensors. The process of recording and evaluating this electrical muscle activity is called electromyography (EMG). Currently, there are two main sensing techniques to measure electromyography signals: intramuscular and surface electrodes. Both sensing techniques allow recording the electrical activity associated with the contraction of muscle ﬁbers in the motor unit being analyzed. The intramuscular electrodes are inserted close to the muscle ﬁbers and can be divided into two groups: wire and needle.