  This study addresses robust automatic speech recognition (ASR) by introducing
a Conformer-based acoustic model. The proposed model builds on the wide
residual bi-directional long short-term memory network (WRBN) with
utterance-wise dropout and iterative speaker adaptation, but employs a
Conformer encoder instead of the recurrent network. The Conformer encoder uses
a convolution-augmented attention mechanism for acoustic modeling. The proposed
system is evaluated on the monaural ASR task of the CHiME-4 corpus. Coupled
with utterance-wise normalization and speaker adaptation, our model achieves
$6.25\%$ word error rate, which outperforms WRBN by $8.4\%$ relatively. In
addition, the proposed Conformer-based model is $18.3\%$ smaller in model size
and reduces total training time by $79.6\%$.
