A Primary Research on Gabor Tensor Sparse Features Representation for Whispered Speech Recognition

DOI

10.2991/eame-15.2015.96How to use a DOI?

Keywords

speech recognition; whispered speech; Gabor filtering; feature extraction

Abstract

Due to differences between normal and whispered speech, traditional feature performed poorly for whispered recognition. In this paper, a novel approach for whispered speech feature representation is proposed based on Gabor filtering and tensor factorization. The sparse feature is extracted by processing the data samples in tensor structure. The simulation results indicate that our proposed feature is able to improve the whispered speech recognition performance.

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  - X.Q. Chen
AU  - H.M. Zhao
AU  - Y.B. Yu
AU  - H.W. Wu
AU  - Z. Liu
PY  - 2015/07
DA  - 2015/07
TI  - A Primary Research on Gabor Tensor Sparse Features Representation for Whispered Speech Recognition
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 346
EP  - 348
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.96
DO  - 10.2991/eame-15.2015.96
ID  - Chen2015/07
ER  -