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

Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition