Language models (LM) have played crucial roles in automatic speech recognition (ASR) to enhance end-to-end (E2E) ASR systems’ performance. There are two categories of approaches: finding better ways to integrate LMs into ASR systems and adapting on LMs to the task domain. This article will start with a reflection of interpolation-based integration methods of E2E ASR’s scores and LM’s scores. Then we will focus on LM augmentation approaches based on the noisy channel model, which is intrigued by insights obtained from the above reflection. The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data. This implies the decoder of an E2E model can be treated as an LM and reveals the possibility of enhancing the E2E model without an external LM. Based on those ideas, we proposed the implicit language model canceling method and then did more discussion about the decoder part of an E2E ASR model. The experimental results on the TED-LIUM2 dataset show that our approach achieves a 3.4% relative WER reduction compared with the baseline system, and more analytic experiments provide concrete experimental supports for our assumption.