  Recently multi-channel end-to-end (ME2E) ASR systems have emerged. While
streaming single-channel end-to-end ASR has been extensively studied, streaming
ME2E ASR is limited in exploration. Additionally, recent studies call attention
to the gap between in-distribution (ID) and out-of-distribution (OOD) tests and
doing realistic evaluations. This paper focuses on two research problems:
realizing streaming ME2E ASR and improving OOD generalization. We propose the
CUSIDE-array method, which integrates the recent CUSIDE methodology (Chunking,
Simulating Future Context and Decoding) into the neural beamformer approach of
ME2E ASR. It enables streaming processing of both front-end and back-end with a
total latency of 402ms. The CUSIDE-array ME2E models are shown to achieve
superior streaming results in both ID and OOD tests. Realistic evaluations
confirm the advantage of CUSIDE-array in its capability to consume
single-channel data to improve OOD generalization via back-end pre-training and
ME2E fine-tuning.
