The rapid development of modern technology and society has raised the need for a large number of real-time translations, such as Automatic Speech Recognition (ASR) technology. One of the important factors determining the fluency of cooperation is that ASR translates the input speech into readable text so that both parties can understand each other's meaning. There is also a huge demand for and reliance on automatic speech recognition technology for modern applications. This article focuses on the word matching algorithm for backward search (WMA-BS), which requires two separate steps, one for the word matching algorithm and the other for the backward search step, for the first step, which can also be done by applying the word-level N-Gram language model. n-Gram model is a language model in which when n words appear in a text, we can use these n words to predict the structure of the text. we can use these n words to predict the structure of the text. In order to make the word-level N-gram language model suitable for the method, it is more useful for us to integrate it into shallow fusion. One of the most important parts to be addressed in the word matching algorithm is the boundary, or distance, between two words in English.