  Silent speech interfaces (SSI) aim to reconstruct the speech signal from a
recording of the articulatory movement, such as an ultrasound video of the
tongue. Currently, deep neural networks are the most successful technology for
this task. The efficient solution requires methods that do not simply process
single images, but are able to extract the tongue movement information from a
sequence of video frames. One option for this is to apply recurrent neural
structures such as the long short-term memory network (LSTM) in combination
with 2D convolutional neural networks (CNNs). Here, we experiment with another
approach that extends the CNN to perform 3D convolution, where the extra
dimension corresponds to time. In particular, we apply the spatial and temporal
convolutions in a decomposed form, which proved very successful recently in
video action recognition. We find experimentally that our 3D network
outperforms the CNN+LSTM model, indicating that 3D CNNs may be a feasible
alternative to CNN+LSTM networks in SSI systems.
