  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.
