  This technical report describes our submission to the 2021 SLT Children
Speech Recognition Challenge (CSRC) Track 1. Our approach combines the use of a
joint CTC-attention end-to-end (E2E) speech recognition framework, transfer
learning, data augmentation and development of various language models.
Procedures of data pre-processing, the background and the course of system
development are described. The analysis of the experiment results, as well as
the comparison between the E2E and DNN-HMM hybrid system are discussed in
detail. Our system achieved a character error rate (CER) of 20.1% in our
designated test set, and 23.6% in the official evaluation set, which is placed
at 10-th overall.
