Article,
Updating the Silent Speech Challenge benchmark with deep learning
Speech Commun., (April 2018)
Abstract
The term ``Silent Speech Interface'' was introduced almost a
decade ago to describe speech communication systems using only
non-acoustic sensors, such as electromyography, ultrasound tongue
imaging, or electromagnetic articulography. Although the use of
specialized sensors in speech processing is challenging, silent
speech research remains an active field that can often profit
from new developments in traditional acoustic speech processing
-- for example recent advances in Deep Learning. After an
overview of Silent Speech Interfaces and their special
challenges, the article presents new results in which a 2010
benchmark study, called the Silent Speech Challenge, is updated
with a Deep Learning strategy, using the same input features and
decoding strategy as in the original Challenge article. A Word
Error Rate of 6.4\% is obtained with the new method, compared to
the published benchmark 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 furthermore been updated to contain
both the original and the new auto-encoder features, in addition
to the original raw data.
