Abstract
The performance of speech recognition systems is commonly
degraded by either speech-related disabilities or by real-world
factors such as the environmentpsilas noise level and
reverberation. In this work, we propose a subvocal speech
recognition system based on EMG signal for subvocal acquisition,
Independent Component Analysis (ICA) for feature extraction and
Neural Networks for classification. We have evaluated the
systempsilas performance using a vowel phonemes database. The
success rate was 93,99\%.
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