Deep Feature Learning for Tibetan Speech Recognition using Sparse Auto-encoder

DOI

10.2991/eame-15.2015.95How to use a DOI?

Keywords

deep feature learning; sparse auto-encoder; tibetan speech recognition; MFCC features

Abstract

HMM models based on MFCC features are widely used by researchers in Tibetan speech recognition. Although the shallow models of HMM are effective, they cannot reflect the speech perceptual mechanism in human being’s brain. In this paper, we propose to apply sparse auto-encoder to learn deep features based on MFCC features for speech data. The deep features not only simulate sparse touches signal of the auditory nerve, and are significant to improve speech recognition accuracy with HMM models. Experimental results show that the deep features learned by sparse auto-encoder perform better on Tibetan speech recognition than MFCC features and the deep features learned by MLP.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - H. Wang
AU  - Y. Zhao
AU  - X.F. Liu
AU  - X.N. Xu
AU  - L. Wang
AU  - N. Zhou
AU  - Y.M. Xu
PY  - 2015/07
DA  - 2015/07
TI  - Deep Feature Learning for Tibetan Speech Recognition using Sparse Auto-encoder
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 342
EP  - 345
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.95
DO  - 10.2991/eame-15.2015.95
ID  - Wang2015/07
ER  -