Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition

Xin Zheng; Zhiyong Wu; Helen Meng; Weifeng Li; Lianhong Cai

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arXiv · 1309.6176

Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition

Xin Zheng·Zhiyong Wu·Helen Meng·Weifeng Li·Lianhong Cai

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Abstract

In this paper, we first present a new variant of Gaussian restricted Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann machine (MGRBM), with its definition and learning algorithm. Then we propose using a learned GRBM or MGRBM to extract better features for robust speech recognition. Our experiments on Aurora2 show that both GRBM-extracted and MGRBM-extracted feature performs much better than Mel-frequency cepstral coefficient (MFCC) with either HMM-GMM or hybrid HMM-deep neural network (DNN) acoustic model, and MGRBM-extracted feature is slightly better.

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BibTeX RIS

Xin Zheng, Zhiyong Wu, Helen Meng, Weifeng Li, Lianhong Cai. 2013-09-23. Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition. https://arxiv.org/abs/1309.6176

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