  Although mask-based beamforming is a powerful speech enhancement approach, it
often requires manual parameter tuning to handle moving speakers. Recently,
this approach was augmented with an attention-based spatial covariance matrix
aggregator (ASA) module, enabling accurate tracking of moving speakers without
manual tuning. However, the deep neural network model used in this module is
limited to specific microphone arrays, necessitating a different model for
varying channel permutations, numbers, or geometries. To improve the robustness
of the ASA module against such variations, in this paper we investigate three
approaches: training with random channel configurations, employing the
transform-average-concatenate method to process multi-channel input features,
and utilizing robust input features. Our experiments on the CHiME-3 and DEMAND
datasets show that these approaches enable the ASA-augmented beamformer to
track moving speakers across different microphone arrays unseen in training.
