THE ROYALFLUSH AUTOMATIC SPEECH DIARIZATION AND RECOGNITION SYSTEM FOR IN-CAR MULTI-CHANNEL AUTOMATIC SPEECH RECOGNITION CHALLENGE

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.