The USTC-NERCSLIP Systems for The ICMC-ASR Challenge
Abstract
This report describes the submitted system to the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) challenge, which considers the ASR task with multi-speaker overlapping and Mandarin accent dynamics in the ICMC case. We implement the front-end speaker diarization using the self-supervised learning representation based multi-speaker embedding and beamforming using the speaker position, respectively. For ASR, we employ an iterative pseudo-label generation method based on fusion model to obtain text labels of unsupervised data. To mitigate the impact of accent, an Accent-ASR framework is proposed, which captures pronunciation-related accent features at a fine-grained level and linguistic information at a coarse-grained level. On the ICMC-ASR eval set, the proposed system achieves a CER of 13.16% on track 1 and a cpCER of 21.48% on track 2, which significantly outperforms the official baseline system and obtains the first rank on both tracks.
Cite
@article{arxiv.2407.02052,
title = {The USTC-NERCSLIP Systems for The ICMC-ASR Challenge},
author = {Minghui Wu and Luzhen Xu and Jie Zhang and Haitao Tang and Yanyan Yue and Ruizhi Liao and Jintao Zhao and Zhengzhe Zhang and Yichi Wang and Haoyin Yan and Hongliang Yu and Tongle Ma and Jiachen Liu and Chongliang Wu and Yongchao Li and Yanyong Zhang and Xin Fang and Yue Zhang},
journal= {arXiv preprint arXiv:2407.02052},
year = {2024}
}
Comments
Accepted at ICASSP 2024