  This paper summarizes the JHU team's efforts in tracks 1 and 2 of the CHiME-6
challenge for distant multi-microphone conversational speech diarization and
recognition in everyday home environments. We explore multi-array processing
techniques at each stage of the pipeline, such as multi-array guided source
separation (GSS) for enhancement and acoustic model training data, posterior
fusion for speech activity detection, PLDA score fusion for diarization, and
lattice combination for automatic speech recognition (ASR). We also report
results with different acoustic model architectures, and integrate other
techniques such as online multi-channel weighted prediction error (WPE)
dereverberation and variational Bayes-hidden Markov model (VB-HMM) based
overlap assignment to deal with reverberation and overlapping speakers,
respectively. As a result of these efforts, our ASR systems achieve a word
error rate of 40.5% and 67.5% on tracks 1 and 2, respectively, on the
evaluation set. This is an improvement of 10.8% and 10.4% absolute, over the
challenge baselines for the respective tracks.
