By João Freitas, António Teixeira, Miguel Sales Dias, Samuel Silva
This ebook presents a large and entire assessment of the prevailing technical ways within the quarter of silent speech interfaces (SSI), either in thought and in software. every one process is defined within the context of the human speech construction procedure, permitting the reader to obviously comprehend the rules in the back of SSI normally and throughout diverse equipment. also, the publication explores the mixed use of other info resources, accrued from quite a few sensors, so one can take on the constraints of less complicated SSI methods, addressing present demanding situations of this box. The e-book additionally offers information regarding latest SSI functions, assets and an easy instructional on how one can construct an SSI.
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Additional resources for An Introduction to Silent Speech Interfaces
1), coupled with magnetic sensors positioned around the user’s head (Fagan et al. 2008; Gilbert et al. 2010; Hofe et al. 2013b), referred to as permanent magnetic articulography (PMA) (Hofe et al. 2013a). In contrast to EMA, PMA does not provide exact locations of the markers, as it is not yet possible to separate the signals of individual magnets from the overall magnetic field. Instead, the authors (Hofe et al. 2013a) look at the detected patterns of magnetic field fluctuations, which are associated with specific articulatory gestures through statistical modelling.
2010; Zhu 2008). This technique is based on the emission of a pure tone in the ultrasound range towards the speaker’s face. The reflected signal is received by an ultrasound sensor tuned to the transmitted frequency, which will contain Doppler frequency shifts proportional to the movements of the speaker’s face. Based on the analysis of the Doppler signal, patterns of movements of the facial muscles, lips, tongue, jaw, etc. can be extracted (Toth et al. 2010). 5), where a source T emits a wave with frequency f0 that is reflected by the moving object, in this case the speaker’s face.
5 % word error rate (WER). In terms of UDS signal analysis, Livescu et al. (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.
An Introduction to Silent Speech Interfaces by João Freitas, António Teixeira, Miguel Sales Dias, Samuel Silva