  Casual conversations involving multiple speakers and noises from surrounding
devices are common in everyday environments, which degrades the performances of
automatic speech recognition systems. These challenging characteristics of
environments are the target of the CHiME-5 challenge. By employing a
convolutional neural network (CNN)-based multichannel end-to-end speech
recognition system, this study attempts to overcome the presents difficulties
in everyday environments. The system comprises of an attention-based
encoder-decoder neural network that directly generates a text as an output from
a sound input. The multichannel CNN encoder, which uses residual connections
and batch renormalization, is trained with augmented data, including white
noise injection. The experimental results show that the word error rate is
reduced by 8.5% and 0.6% absolute from a single channel end-to-end and the best
baseline (LF-MMI TDNN) on the CHiME-5 corpus, respectively.
