  Noisy Student Training (NST) has recently demonstrated extremely strong
performance in Automatic Speech Recognition(ASR). In this paper, we propose a
data selection strategy named LM Filter to improve the performance of NST on
non-target domain data in ASR tasks. Hypotheses with and without a Language
Model are generated and the CER differences between them are utilized as a
filter threshold. Results reveal that significant improvements of 10.4%
compared with no data filtering baselines. We can achieve 3.31% CER in
AISHELL-1 test set, which is best result from our knowledge without any other
supervised data. We also perform evaluations on the supervised 1000 hour
AISHELL-2 dataset and competitive results of 4.73% CER can be achieved.
