The USTC-NERCSLIP Systems for The ICMC-ASR Challenge
<|reference_start|>The USTC-NERCSLIP Systems for The ICMC-ASR Challenge: This report describes the submitted system to the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) challenge, which considers the ASR task with multi-speaker overlapping and Mandarin accent dynamics in the ICMC case. We implement the front-end speaker diarization using the self-supervised learning representation based multi-speaker embedding and beamforming using the speaker position, respectively. For ASR, we employ an iterative pseudo-label generation method based on fusion model to obtain text labels of unsupervised data. To mitigate the impact of accent, an Accent-ASR framework is proposed, which captures pronunciation-related accent features at a fine-grained level and linguistic information at a coarse-grained level. On the ICMC-ASR eval set, the proposed system achieves a CER of 13.16% on track 1 and a cpCER of 21.48% on track 2, which significantly outperforms the official baseline system and obtains the first rank on both tracks.<|reference_end|>
@article{wu2024the,
    title={The USTC-NERCSLIP Systems for The ICMC-ASR Challenge},
    author={Minghui Wu, Luzhen Xu, Jie Zhang, Haitao Tang, Yanyan Yue, Ruizhi
  Liao, Jintao Zhao, Zhengzhe Zhang, Yichi Wang, Haoyin Yan, Hongliang Yu,
  Tongle Ma, Jiachen Liu, Chongliang Wu, Yongchao Li, Yanyong Zhang, Xin Fang,
  Yue Zhang},
    journal={arXiv preprint arXiv:2407.02052},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02052},
    primaryClass={eess.AS cs.SD}
}