  It is critical for a keyword spotting model to have a small footprint as it
typically runs on-device with low computational resources. However, maintaining
the previous SOTA performance with reduced model size is challenging. In
addition, a far-field and noisy environment with multiple signals interference
aggravates the problem causing the accuracy to degrade significantly. In this
paper, we present a multi-channel ConvMixer for speech command recognitions.
The novel architecture introduces an additional audio channel mixing for
channel audio interaction in a multi-channel audio setting to achieve better
noise-robust features with more efficient computation. Besides, we proposed a
centroid based awareness component to enhance the system by equipping it with
additional spatial geometry information in the latent feature projection space.
We evaluate our model using the new MISP challenge 2021 dataset. Our model
achieves significant improvement against the official baseline with a 55% gain
in the competition score (0.152) on raw microphone array input and a 63%
(0.126) boost upon front-end speech enhancement.
