Updating the silent speech challenge benchmark with deep learning

September 20, 2017 ยท Declared Dead ยท ๐Ÿ› Speech Communication

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Authors Yan Ji, Licheng Liu, Hongcui Wang, Zhilei Liu, Zhibin Niu, Bruce Denby arXiv ID 1709.06818 Category cs.CL: Computation & Language Cross-listed cs.CV, cs.HC Citations 58 Venue Speech Communication Last Checked 4 months ago
Abstract
The 2010 Silent Speech Challenge benchmark is updated with new results obtained in a Deep Learning strategy, using the same input features and decoding strategy as in the original article. A Word Error Rate of 6.4% is obtained, compared to the published value of 17.4%. Additional results comparing new auto-encoder-based features with the original features at reduced dimensionality, as well as decoding scenarios on two different language models, are also presented. The Silent Speech Challenge archive has been updated to contain both the original and the new auto-encoder features, in addition to the original raw data.
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