  Building a good speech recognition system usually requires large amounts of
transcribed data, which is expensive to collect. To tackle this problem, many
unsupervised pre-training methods have been proposed. Among these methods,
Masked Predictive Coding achieved significant improvements on various speech
recognition datasets with BERT-like Masked Reconstruction loss and Transformer
backbone. However, many aspects of MPC have not been fully investigated. In
this paper, we conduct a further study on MPC and focus on three important
aspects: the effect of pre-training data speaking style, its extension on
streaming model, and how to better transfer learned knowledge from pre-training
stage to downstream tasks. Experiments reveled that pre-training data with
matching speaking style is more useful on downstream recognition tasks. A
unified training objective with APC and MPC provided 8.46% relative error
reduction on streaming model trained on HKUST. Also, the combination of target
data adaption and layer-wise discriminative training helped the knowledge
transfer of MPC, which achieved 3.99% relative error reduction on AISHELL over
a strong baseline.
