تقرير
Research on an improved Conformer end-to-end Speech Recognition Model with R-Drop Structure
| العنوان: | Research on an improved Conformer end-to-end Speech Recognition Model with R-Drop Structure |
|---|---|
| المؤلفون: | Ji, Weidong, Zan, Shijie, Zhou, Guohui, Wang, Xu |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. Comment: 15 pages, 9 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.08329 |
| رقم الأكسشن: | edsarx.2306.08329 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |