  We propose a novel deep neural network architecture for speech recognition
that explicitly employs knowledge of the background environmental noise within
a deep neural network acoustic model. A deep neural network is used to predict
the acoustic environment in which the system in being used. The discriminative
embedding generated at the bottleneck layer of this network is then
concatenated with traditional acoustic features as input to a deep neural
network acoustic model. Through a series of experiments on Resource Management,
CHiME-3 task, and Aurora4, we show that the proposed approach significantly
improves speech recognition accuracy in noisy and highly reverberant
environments, outperforming multi-condition training, noise-aware training,
i-vector framework, and multi-task learning on both in-domain noise and unseen
noise.
