Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition
<|reference_start|>Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition: Currently, end-to-end (E2E) speech recognition methods have achieved promising performance. However, auto speech recognition (ASR) models still face challenges in recognizing multi-accent speech accurately. We propose a layer-adapted fusion (LAF) model, called Qifusion-Net, which does not require any prior knowledge about the target accent. Based on dynamic chunk strategy, our approach enables streaming decoding and can extract frame-level acoustic feature, facilitating fine-grained information fusion. Experiment results demonstrate that our proposed methods outperform the baseline with relative reductions of 22.1$\%$ and 17.2$\%$ in character error rate (CER) across multi accent test datasets on KeSpeech and MagicData-RMAC.<|reference_end|>
@article{chen2024qifusion-net:,
    title={Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End
  Multi-Accent Speech Recognition},
    author={Jinming Chen, Jingyi Fang, Yuanzhong Zheng, Yaoxuan Wang, Haojun Fei},
    journal={arXiv preprint arXiv:2407.03026},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03026},
    primaryClass={cs.SD cs.AI eess.AS}
}