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.

翻译：本技术报告详细介绍了我们提交至CHiME-7 DASR挑战赛的系统，该挑战赛专注于复杂多说话人场景下的说话人日记化与语音识别。此外，该挑战赛还评估了系统处理多种阵列设备的效率。为应对这些问题，我们实现了一个端到端的说话人日记化系统，并引入了一种基于多通道空间信息的修正策略。该方法显著降低了词错误率（WER）。在识别方面，我们利用公开可用的预训练模型作为基础模型，训练了端到端的语音识别模型。我们的系统在CHiME-7评估集上实现了21.01%的宏平均日记化归属词错误率（DA-WER），相较官方基线系统相对提升了62.04%。