Grammatical Error Correction Detection of English Conversational Pronunciation Under a Deep Learning Algorithm

Abstract

Grammar correction in spoken English can enhance proficiency. This paper briefly introduces the gate recurrent unit (GRU) algorithm and its application in English speech recognition and grammatical error correction of speech recognition results. The GRU algorithm was firstly used to recognize English speech, then transform it into a text, and finally correct the English grammar of the text. Additionally, the attention mechanism was incorporated to enhance the performance of grammatical error correction. Subsequently, simulation experiments were conducted. Firstly, speech recognition and grammatical error correction were independently verified. The performance of the proposed algorithm in correcting grammatical errors in spoken English was evaluated using a self-built speech database. The results demonstrated that the proposed GRU-based algorithm yielded the best performance in independent speech recognition, independent grammatical error correction, and the overall spoken grammatical error correction. The contribution of this study lies in using the GRU algorithm to convert speech into text and perform grammar correction on the text, providing an effective reference for grammar correction in English communication.