This paper proposes a method to relax the conditional independence assumption
of connectionist temporal classification (CTC)-based automatic speech
recognition (ASR) models. We train a CTC-based ASR model with auxiliary CTC
losses in intermediate layers in addition to the original CTC loss in the last
layer. During both training and inference, each generated prediction in the
intermediate layers is summed to the input of the next layer to condition the
prediction of the last layer on those intermediate predictions. Our method is
easy to implement and retains the merits of CTC-based ASR: a simple model
architecture and fast decoding speed. We conduct experiments on three different
ASR corpora. Our proposed method improves a standard CTC model significantly
(e.g., more than 20 % relative word error rate reduction on the WSJ corpus)
with a little computational overhead. Moreover, for the TEDLIUM2 corpus and the
AISHELL-1 corpus, it achieves a comparable performance to a strong
autoregressive model with beam search, but the decoding speed is at least 30
times faster.

本文提出了一种方法来放宽 CTC-based 自动语音识别 (ASR) 模型的条件独立性假设，通过在中间层中添加辅助的 CTC loss，使预测更准确，减少了相对词错误率超过 20%。