Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Denoising convolutional autoencoder based B-mode ultrasound tongue image
  feature extraction   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.

34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas