New Parallelized Greedy Soup Algorithm in the End-to-End Automatic Speech Recognition

This paper proposed a new algorithm in the end-to-end automatic speech recognition. 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.