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To address the issue of poor generalization ability in end-to-end speechrecognition models within deep learning, this study proposes a newConformer-based speech recognition model called "Conformer-R" that incorporatesthe R-drop structure. This model combines the Conformer model, which has shownpromising results in speech recognition, with the R-drop structure. By doingso, the model is able to effectively model both local and global speechinformation while also reducing overfitting through the use of the R-dropstructure. This enhances the model's ability to generalize and improves overallrecognition efficiency. The model was first pre-trained on the Aishell1 andWenetspeech datasets for general domain adaptation, and subsequently fine-tunedon computer-related audio data. Comparison tests with classic models such asLAS and Wenet were performed on the same test set, demonstrating theConformer-R model's ability to effectively improve generalization.

Research on an improved Conformer end-to-end Speech Recognition Model with R-Drop Structure