  This paper summarizes our acoustic modeling efforts in the Johns Hopkins
University speech recognition system for the CHiME-5 challenge to recognize
highly-overlapped dinner party speech recorded by multiple microphone arrays.
We explore data augmentation approaches, neural network architectures,
front-end speech dereverberation, beamforming and robust i-vector extraction
with comparisons of our in-house implementations and publicly available tools.
We finally achieved a word error rate of 69.4% on the development set, which is
a 11.7% absolute improvement over the previous baseline of 81.1%, and release
this improved baseline with refined techniques/tools as an advanced CHiME-5
recipe.
