Research on an improved Conformer end-to-end Speech Recognition Model with R-Drop Structure
This work addresses generalization issues in speech recognition models, particularly for computer-related audio data, but it appears incremental as it combines existing Conformer and R-drop components.
The study tackled poor generalization in end-to-end speech recognition by proposing a Conformer-R model with an R-drop structure, which improved generalization and recognition efficiency as demonstrated in comparison tests with models like LAS and Wenet on a test set.
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.