In spite of various observation tools, tongue shapes
are still scarce resource in reality. Autoencoder, a kind of deep
neural networks (DNN), performs well on data reduction and
pattern discovery. However, since autoencoder usually needs
large scale data in training, challenges exist for traditional
autoencoder to obtain tongues' motion patterns only from tens or
hundreds of available tongue shapes. To overcome this problem,
we propose a two-steps autoencoder, where we first construct a
stacked denoising autoencoder (dAE) to learn the essential
presentation of the tongue shapes from their possible
deformations; then an additional autoencoder with small number
of hidden units is added upon the previous stacked autoencoder,
and used for dimensionality reduction. Experiments run on 240
vowels' tongue shapes obtained from Chinese speakers'
pronunciation X-ray films, and the proposed model is compared
with traditional dAE and the classical principal component
analysis (PCA) on dimensionality reduction and reconstruction in
details. Results validate the performance of the proposed tongue
model.