The RoyalFlush Automatic Speech Diarization and Recognition System for In-Car Multi-Channel Automatic Speech Recognition Challenge
<|reference_start|>The RoyalFlush Automatic Speech Diarization and Recognition System for In-Car Multi-Channel Automatic Speech Recognition Challenge: This paper presents our system submission for the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) Challenge, which focuses on speaker diarization and speech recognition in complex multi-speaker scenarios. To address these challenges, we develop end-to-end speaker diarization models that notably decrease the diarization error rate (DER) by 49.58\% compared to the official baseline on the development set. For speech recognition, we utilize self-supervised learning representations to train end-to-end ASR models. By integrating these models, we achieve a character error rate (CER) of 16.93\% on the track 1 evaluation set, and a concatenated minimum permutation character error rate (cpCER) of 25.88\% on the track 2 evaluation set.<|reference_end|>
@article{tian2024the,
    title={The RoyalFlush Automatic Speech Diarization and Recognition System for
  In-Car Multi-Channel Automatic Speech Recognition Challenge},
    author={Jingguang Tian, Shuaishuai Ye, Shunfei Chen, Yang Xiang, Zhaohui Yin,
  Xinhui Hu, Xinkang Xu},
    journal={arXiv preprint arXiv:2405.05498},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05498},
    primaryClass={cs.SD eess.AS}
}