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An electromygraphic (EMG) Silent Speech Interface is a system which recognizes speech by capturing the electric potentials of the human articulatory muscles, thus enabling the user to communicate silently. This study is concerned with introducing an EMG recording system based on multi-channel electrode arrays. We first present our new system and introduce a(More)
This paper reports on our latest study on speech recognition based on surface electromyography (EMG). This technology allows for Silent Speech Interfaces since EMG captures the electrical potentials of the human articulatory muscles rather than the acoustic speech signal. Therefore, our technology enables speech recognition to be applied to silently mouthed(More)
In this paper we present our recent study on the impact of speaking mode variabilities on speech recognition by surface elec-tromyography (EMG). Surface electromyography captures the electric potentials of the human articulatory muscles, which enables a user to communicate naturally without making any audible sound. Our previous experiments have shown that(More)
In a quantitative manner, we investigated the mechanism of switching ezrin from the dormant to the active, F-actin binding state by recognition of PIP 2. For this purpose, we established a novel in vitro model mimicking ezrin-mediated membrane-cytoskeleton attachment and compared the F-actin binding capability of ezrin that either had been coupled via a His(More)
This paper presents our recent advances in speech recognition based on surface electromyography (EMG). This technology allows for Silent Speech Interfaces since EMG captures the electrical potentials of the human articulatory muscles rather than the acoustic speech signal. Our earlier experiments have shown that the EMG signal is greatly impacted by the(More)
This paper reports on our recent research in the feedback effects of Silent Speech. Our technology is based on surface elec-tromyography (EMG) which captures the electrical potentials of the human articulatory muscles rather than the acoustic speech signal. While recognition results are good for loudly articulated speech and when experienced users speak(More)
This study is concerned with the impact of speaking mode variabilities on speech recognition by surface electromyography (EMG). In EMG-based speech recognition, we capture the electric potentials of the human articulatory muscles by surface electrodes, so that the resulting signal can be used for speech processing. This enables the user to communicate(More)
We introduce a spatial artifact detection method for a surface electromyography (EMG) based speech recognition system. The EMG signals are recorded using grid-shaped electrode arrays affixed to the speakers face. Continuous speech recognition is performed on the basis of these signals. As the EMG data are high-dimensional, Independent Component Analysis(More)