  B-mode ultrasound tongue imaging is widely used in the speech production
field. However, efficient interpretation is in a great need for the tongue
image sequences. Inspired by the recent success of unsupervised deep learning
approach, we explore unsupervised convolutional network architecture for the
feature extraction in the ultrasound tongue image, which can be helpful for the
clinical linguist and phonetics. By quantitative comparison between different
unsupervised feature extraction approaches, the denoising convolutional
autoencoder (DCAE)-based method outperforms the other feature extraction
methods on the reconstruction task and the 2010 silent speech interface
challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the
state-of-the-art value of 6.45% using Discrete cosine transform as the feature
extractor. Our codes are available at
https://github.com/DeePBluE666/Source-code1.
