  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.
