  In this paper, we focus on the task of small-footprint keyword spotting under
the far-field scenario. Far-field environments are commonly encountered in
real-life speech applications, causing severe degradation of performance due to
room reverberation and various kinds of noises. Our baseline system is built on
the convolutional neural network trained with pooled data of both far-field and
close-talking speech. To cope with the distortions, we develop three domain
aware training systems, including the domain embedding system, the deep CORAL
system, and the multi-task learning system. These methods incorporate domain
knowledge into network training and improve the performance of the keyword
classifier on far-field conditions. Experimental results show that our proposed
methods manage to maintain the performance on the close-talking speech and
achieve significant improvement on the far-field test set.
