Research on an improved Conformer end-to-end Speech Recognition Model
  with R-Drop Structure
Weidong Ji Shijie Zan Guohui Zhou Xu Wang
To address the issue of poor generalization ability in end-to-end speech
recognition models within deep learning, this study proposes a new
Conformer-based speech recognition model called "Conformer-R" that incorporates
the R-drop structure. This model combines the Conformer model, which has shown
promising results in speech recognition, with the R-drop structure. By doing
so, the model is able to effectively model both local and global speech
information while also reducing overfitting through the use of the R-drop
structure. This enhances the model's ability to generalize and improves overall
recognition efficiency. The model was first pre-trained on the Aishell1 and
Wenetspeech datasets for general domain adaptation, and subsequently fine-tuned
on computer-related audio data. Comparison tests with classic models such as
LAS and Wenet were performed on the same test set, demonstrating the
Conformer-R model's ability to effectively improve generalization.