<p>This paper presents a new method for calculating similarity. It's called the unit dot product similarity method. The proposed method, unlike the traditional dot product method, can maintain the similarity of equally scaled vectors and get a bounded similarity results. We develop and compare the proposed method in the attention-based encoder-decoder structure. The proposed method brings further improvement to the recognition results. For the end-to-end speech recognition model, we select greedy soup instead of the average model parameters in WeNet. We proposed a dynamic parallel greedy soup optimization algorithm to increase computational speed. The experiments show the importance of proposed method and optimization algorithm. The effectiveness is also proved on multiple corpora.</p>