CASIA OpenIR  > 人机语音交互团队
Reducing Tongue Shape Dimensionality from Hundreds of Available Resources Using Autoencoder
Minghao Yang1,2; Dawei Zhang1; Jianhua Tao1,2,3
2018
Conference Name2018 24th International Conference on Pattern Recognition (ICPR)
Conference Date2018.08.20-2018.08.24
Conference Place北京
Abstract

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.

KeywordVocal Tract Neural Network Tongue Shape Pca
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26176
Collection人机语音交互团队
Affiliation1.National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences
2.The Center for Excellence in Brain Science and Intelligent Technology of Chinese Academy of Sciences
3.University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Minghao Yang,Dawei Zhang,Jianhua Tao. Reducing Tongue Shape Dimensionality from Hundreds of Available Resources Using Autoencoder[C],2018.
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