  This paper proposes a flexible multichannel speech enhancement system with
the main goal of improving robustness of automatic speech recognition (ASR) in
noisy conditions. The proposed system combines a flexible neural mask estimator
applicable to different channel counts and configurations and a multichannel
filter with automatic reference selection. A transform-attend-concatenate layer
is proposed to handle cross-channel information in the mask estimator, which is
shown to be effective for arbitrary microphone configurations. The presented
evaluation demonstrates the effectiveness of the flexible system for several
seen and unseen compact array geometries, matching the performance of fixed
configuration-specific systems. Furthermore, a significantly improved ASR
performance is observed for configurations with randomly-placed microphones.
