  This paper demonstrates two novel methods to estimate the global SNR of
speech signals. In both methods, Deep Neural Network-Hidden Markov Model
(DNN-HMM) acoustic model used in speech recognition systems is leveraged for
the additional task of SNR estimation. In the first method, the entropy of the
DNN-HMM output is computed. Recent work on bayesian deep learning has shown
that a DNN-HMM trained with dropout can be used to estimate model uncertainty
by approximating it as a deep Gaussian process. In the second method, this
approximation is used to obtain model uncertainty estimates. Noise specific
regressors are used to predict the SNR from the entropy and model uncertainty.
The DNN-HMM is trained on GRID corpus and tested on different noise profiles
from the DEMAND noise database at SNR levels ranging from -10 dB to 30 dB.
