  End-to-end attention-based models have been shown to be competitive
alternatives to conventional DNN-HMM models in the Speech Recognition Systems.
In this paper, we extend existing end-to-end attention-based models that can be
applied for Distant Speech Recognition (DSR) task. Specifically, we propose an
end-to-end attention-based speech recognizer with multichannel input that
performs sequence prediction directly at the character level. To gain a better
performance, we also incorporate Highway long short-term memory (HLSTM) which
outperforms previous models on AMI distant speech recognition task.
