2020
INTERSPEECH
INTERSPEECH 2020
CSL-EMG_Array: An Open Access Corpus for EMG-to-Speech Conversion
Abstract
We present a new open access corpus for the training and evaluation of EMG-to-Speech conversion systems based on array electromyographic recordings. The corpus is recorded with a recording paradigm closely mirroring realistic EMG-to-Speech usage scenarios, and includes evaluation data recorded from both audible as well as silent speech. The corpus consists of 9.5 hours of data, split into 12 sessions recorded from 8 speakers. Based on this corpus, we present initial benchmark results with a realistic online EMG-to-Speech conversion use case, both for the audible and silent speech subsets. We also present a method for drastically improving EMG-to-Speech system stability and performance in the presence of time-related artifacts.
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Trend Setter
— Data Mining
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Keyword Pioneer
— electromyography signal
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Natural Language Processing, Speech & Audio
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Interdisciplinary Bridge
— Artificial Intelligence and Data Science & Analytics and Speech & Audio