  In this paper, the uncertainty is defined as the mean square error between a
given enhanced noisy observation vector and the corresponding clean one. Then,
a DNN is trained by using enhanced noisy observation vectors as input and the
uncertainty as output with a training database. In testing, the DNN receives an
enhanced noisy observation vector and delivers the estimated uncertainty. This
uncertainty in employed in combination with a weighted DNN-HMM based speech
recognition system and compared with an existing estimation of the noise
cancelling uncertainty variance based on an additive noise model. Experiments
were carried out with Aurora-4 task. Results with clean, multi-noise and
multi-condition training are presented.
