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 settings. 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 22.4\% on the CHiME-7 development set, which signifies a relative improvement of 52.5\% over the official baseline system.

翻译：本技术报告详细介绍了我们为CHiME-7 DASR挑战赛提交的系统，该挑战赛聚焦于复杂多说话人场景下的说话人日志化与语音识别。此外，该挑战赛还评估了系统处理多样化阵列设备的效率。为解决这些问题，我们实现了一个端到端说话人日志化系统，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，训练了端到端语音识别模型。我们的系统在CHiME-7开发集上实现了22.4%的宏平均日志化属性词错误率（DA-WER），相较于官方基线系统取得了52.5%的相对提升。