  End-to-end (E2E) multi-channel ASR systems show state-of-the-art performance
in far-field ASR tasks by joint training of a multi-channel front-end along
with the ASR model. The main limitation of such systems is that they are
usually trained with data from a fixed array geometry, which can lead to
degradation in accuracy when a different array is used in testing. This makes
it challenging to deploy these systems in practice, as it is costly to retrain
and deploy different models for various array configurations. To address this,
we present a simple and effective data augmentation technique, which is based
on randomly dropping channels in the multi-channel audio input during training,
in order to improve the robustness to various array configurations at test
time. We call this technique ChannelAugment, in contrast to SpecAugment (SA)
which drops time and/or frequency components of a single channel input audio.
We apply ChannelAugment to the Spatial Filtering (SF) and Minimum Variance
Distortionless Response (MVDR) neural beamforming approaches. For SF, we
observe 10.6% WER improvement across various array configurations employing
different numbers of microphones. For MVDR, we achieve a 74% reduction in
training time without causing degradation of recognition accuracy.
