  In this paper, we present an improved model for voicing silent speech, where
audio is synthesized from facial electromyography (EMG) signals. To give our
model greater flexibility to learn its own input features, we directly use EMG
signals as input in the place of hand-designed features used by prior work. Our
model uses convolutional layers to extract features from the signals and
Transformer layers to propagate information across longer distances. To provide
better signal for learning, we also introduce an auxiliary task of predicting
phoneme labels in addition to predicting speech audio features. On an open
vocabulary intelligibility evaluation, our model improves the state of the art
for this task by an absolute 25.8%.
