  Attention-based beamformers have recently been shown to be effective for
multi-channel speech recognition. However, they are less capable at capturing
local information. In this work, we propose a 2D Conv-Attention module which
combines convolution neural networks with attention for beamforming. We apply
self- and cross-attention to explicitly model the correlations within and
between the input channels. The end-to-end 2D Conv-Attention model is compared
with a multi-head self-attention and superdirective-based neural beamformers.
We train and evaluate on an in-house multi-channel dataset. The results show a
relative improvement of 3.8% in WER by the proposed model over the baseline
neural beamformer.
