  In this paper, we propose a novel adaptive technique that uses an
attention-based gated scaling (AGS) scheme to improve deep feature learning for
connectionist temporal classification (CTC) acoustic modeling. In AGS, the
outputs of each hidden layer of the main network are scaled by an auxiliary
gate matrix extracted from the lower layer by using attention mechanisms.
Furthermore, the auxiliary AGS layer and the main network are jointly trained
without requiring second-pass model training or additional speaker information,
such as speaker code. On the Mandarin AISHELL-1 datasets, the proposed AGS
yields a 7.94% character error rate (CER). To the best of our knowledge, this
result is the best recognition accuracy achieved on this dataset by using an
end-to-end framework.
