  Automatic Speech Recognition (ASR) has shown remarkable progress, yet it
still faces challenges in real-world distant scenarios across various array
topologies each with multiple recording devices. The focal point of the CHiME-7
Distant ASR task is to devise a unified system capable of generalizing various
array topologies that have multiple recording devices and offering reliable
recognition performance in real-world environments. Addressing this task, we
introduce an ASR system that demonstrates exceptional performance across
various array topologies. First of all, we propose two attention-based
automatic channel selection modules to select the most advantageous subset of
multi-channel signals from multiple recording devices for each utterance.
Furthermore, we introduce inter-channel spatial features to augment the
effectiveness of multi-frame cross-channel attention, aiding it in improving
the capability of spatial information awareness. Finally, we propose a
multi-layer convolution fusion module drawing inspiration from the U-Net
architecture to integrate the multi-channel output into a single-channel
output. Experimental results on the CHiME-7 corpus with oracle segmentation
demonstrate that the improvements introduced in our proposed ASR system lead to
a relative reduction of 40.1% in the Macro Diarization Attributed Word Error
Rates (DA-WER) when compared to the baseline ASR system on the Eval sets.
