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This paper presents early-stage results of our investigations into the direct conversion of facial surface electromyographic (EMG) signals into audible speech in a real-time setting, enabling novel avenues for research and system improvement through real-time feedback. The system uses a pipeline approach to enable online acquisition of EMG data, extraction of EMG features, mapping of EMG features...
An electromyographic (EMG) silent speech recognizer is a system that recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. After having established a baseline EMG-based continuous speech recognizer, in this paper, we investigate speaking mode variations, i.e., discrepancies between audible and silent speech that deteriorate...
Our study deals with a Silent Speech Interface based on mapping surface electromyographic (EMG) signals to speech waveforms. Electromyographic signals recorded from the facial muscles capture the activity of the human articulatory apparatus and therefore allow to retrace speech, even when no audible signal is produced. The mapping of EMG signals to speech is done via a Gaussian mixture model (GMM)-based...
In this paper we present a study on phone confusabilities based on phone recognition experiments from facial surface electromyographic (EMG) signals. In our study EMG captures the electrical potentials of the human articulatory muscles. This technology can be used to create Silent Speech Interfaces, where a user can communicate naturally without uttering any sound. This paper investigates to which...
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