  The connectionist temporal classification (CTC) enables end-to-end sequence
learning by maximizing the probability of correctly recognizing sequences
during training. The outputs of a CTC-trained model tend to form a series of
spikes separated by strongly predicted blanks, know as the spiky problem. To
figure out the reason for it, we reinterpret the CTC training process as an
iterative fitting task that is based on frame-wise cross-entropy loss. It
offers us an intuitive way to compare target probabilities with model outputs
for each iteration, and explain how the model outputs gradually turns spiky.
Inspired by it, we put forward two ways to modify the CTC training. The
experiments demonstrate that our method can well solve the spiky problem and
moreover, lead to faster convergence over various training settings. Beside
this, the reinterpretation of CTC, as a brand new perspective, may be
potentially useful in other situations. The code is publicly available at
https://github.com/hzli-ucas/caffe/tree/ctc.
