The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge
<|reference_start|>The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge: This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge. The primary difficulty of this challenge is the dataset recorded across various conference rooms, which captures real-world complexities such as high overlap rates, background noises, a variable number of speakers, and natural conversation styles. To address these issues, we optimized the system in several aspects: For front-end speech signal processing, we introduced a data-driven joint training method for diarization and separation (JDS) to enhance audio quality. Additionally, we also integrated traditional guided source separation (GSS) for multi-channel track to provide complementary information for the JDS. For back-end speech recognition, we enhanced Whisper with WavLM, ConvNeXt, and Transformer innovations, applying multi-task training and Noise KLD augmentation, to significantly advance ASR robustness and accuracy. Our system attained a Time-Constrained minimum Permutation Word Error Rate (tcpWER) of 14.265% and 22.989% on the CHiME-8 NOTSOFAR-1 Dev-set-2 multi-channel and single-channel tracks, respectively.<|reference_end|>
@article{niu2024the,
    title={The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge},
    author={Shutong Niu, Ruoyu Wang, Jun Du, Gaobin Yang, Yanhui Tu, Siyuan Wu,
  Shuangqing Qian, Huaxin Wu, Haitao Xu, Xueyang Zhang, Guolong Zhong, Xindi
  Yu, Jieru Chen, Mengzhi Wang, Di Cai, Tian Gao, Genshun Wan, Feng Ma, Jia
  Pan, Jianqing Gao},
    journal={arXiv preprint arXiv:2409.02041},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02041},
    primaryClass={eess.AS cs.SD}
}