  Transformers are powerful neural architectures that allow integrating
different modalities using attention mechanisms. In this paper, we leverage the
neural transformer architectures for multi-channel speech recognition systems,
where the spectral and spatial information collected from different microphones
are integrated using attention layers. Our multi-channel transformer network
mainly consists of three parts: channel-wise self attention layers (CSA),
cross-channel attention layers (CCA), and multi-channel encoder-decoder
attention layers (EDA). The CSA and CCA layers encode the contextual
relationship within and between channels and across time, respectively. The
channel-attended outputs from CSA and CCA are then fed into the EDA layers to
help decode the next token given the preceding ones. The experiments show that
in a far-field in-house dataset, our method outperforms the baseline
single-channel transformer, as well as the super-directive and neural
beamformers cascaded with the transformers.
