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Connectionist temporal classification (CTC) and attention-based encoderdecoder (AED) joint training has been widely applied in automatic speechrecognition (ASR). Unlike most hybrid models that separately calculate the CTCand AED losses, our proposed integrated-CTC utilizes the attention mechanism ofAED 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 theattention results to match the dimensions of CTC. To accelerate modelconvergence and improve accuracy, we introduce auxiliary loss regularizationfor accelerated convergence. Experimental results demonstrate that the DALmethod performs better in attention rescoring, while the PMP method excels inCTC prefix beam search and greedy search.

Improving CTC-AED model with integrated-CTC and auxiliary loss regularization