This technical report details our submission system to the CHiME-7 DASR Challenge, which focuses on speaker diarization and speech recognition under complex multi-speaker scenarios. Additionally, it also evaluates the efficiency of systems in handling diverse array devices. To address these issues, we implemented an end-to-end speaker diarization system and introduced a rectification strategy based on multi-channel spatial information. This approach significantly diminished the word error rates (WER). In terms of recognition, we utilized publicly available pre-trained models as the foundational models to train our end-to-end speech recognition models. Our system attained a Macro-averaged diarization-attributed WER (DA-WER) of 21.01% on the CHiME-7 evaluation set, which signifies a relative improvement of 62.04% over the official baseline system.
@article{arxiv.2308.14638,
title = {The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge},
author = {Ruoyu Wang and Maokui He and Jun Du and Hengshun Zhou and Shutong Niu and Hang Chen and Yanyan Yue and Gaobin Yang and Shilong Wu and Lei Sun and Yanhui Tu and Haitao Tang and Shuangqing Qian and Tian Gao and Mengzhi Wang and Genshun Wan and Jia Pan and Jianqing Gao and Chin-Hui Lee},
journal= {arXiv preprint arXiv:2308.14638},
year = {2023}
}