Improving CTC-AED model with integrated-CTC and auxiliary loss
  regularization
Daobin Zhu Xiangdong Su Hongbin Zhang
Connectionist temporal classification (CTC) and attention-based encoder
decoder (AED) joint training has been widely applied in automatic speech
recognition (ASR). Unlike most hybrid models that separately calculate the CTC
and AED losses, our proposed integrated-CTC utilizes the attention mechanism of
AED to guide the output of CTC. In this paper, we employ two fusion methods,
namely direct addition of logits (DAL) and preserving the maximum probability
(PMP). We achieve dimensional consistency by adaptively affine transforming the
attention results to match the dimensions of CTC. To accelerate model
convergence and improve accuracy, we introduce auxiliary loss regularization
for accelerated convergence. Experimental results demonstrate that the DAL
method performs better in attention rescoring, while the PMP method excels in
CTC prefix beam search and greedy search.