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Because of predicting all the target tokens in parallel, thenon-autoregressive models greatly improve the decoding efficiency of speechrecognition compared with traditional autoregressive models. In this work, wepresent dynamic alignment Mask CTC, introducing two methods: (1) Aligned CrossEntropy (AXE), finding the monotonic alignment that minimizes the cross-entropyloss through dynamic programming, (2) Dynamic Rectification, creating newtraining samples by replacing some masks with model predicted tokens. The AXEignores the absolute position alignment between prediction and ground truthsentence and focuses on tokens matching in relative order. The dynamicrectification method makes the model capable of simulating the non-mask butpossible wrong tokens, even if they have high confidence. Our experiments onWSJ dataset demonstrated that not only AXE loss but also the rectificationmethod could improve the WER performance of Mask CTC.

Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy