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• W2060756806 abstract "An electromyographic (EMG) <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>silent speech recognizer</i> 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 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>speaking mode</i> variations, i.e., discrepancies between audible and silent speech that deteriorate recognition accuracy. We introduce <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>multimode</i> systems that allow seamless switching between audible and silent speech, investigate different measures which quantify speaking mode differences, and present the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>spectral mapping </i> algorithm, which improves the word error rate (WER) on silent speech by up to 14.3% relative. Our best average silent speech WER is 34.7%, and our best WER on audibly spoken speech is 16.8%." @default.
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• W2060756806 date "2014-10-01" @default.
• W2060756806 modified "2023-10-16" @default.
• W2060756806 title "Tackling Speaking Mode Varieties in EMG-Based Speech Recognition" @default.
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• W2060756806 doi "https://doi.org/10.1109/tbme.2014.2319000" @default.
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