The second multi-channel multi-party meeting transcription challenge (M2MeT) 2.0): A benchmark for speaker-attributed ASR
Abstract
With the success of the first Multi-channel Multi-party Meeting Transcription challenge (M2MeT), the second M2MeT challenge (M2MeT 2.0) held in ASRU2023 particularly aims to tackle the complex task of \emph{speaker-attributed ASR (SA-ASR)}, which directly addresses the practical and challenging problem of ``who spoke what at when" at typical meeting scenario. We particularly established two sub-tracks. The fixed training condition sub-track, where the training data is constrained to predetermined datasets, but participants can use any open-source pre-trained model. The open training condition sub-track, which allows for the use of all available data and models without limitation. In addition, we release a new 10-hour test set for challenge ranking. This paper provides an overview of the dataset, track settings, results, and analysis of submitted systems, as a benchmark to show the current state of speaker-attributed ASR.
Cite
@article{arxiv.2309.13573,
title = {The second multi-channel multi-party meeting transcription challenge (M2MeT) 2.0): A benchmark for speaker-attributed ASR},
author = {Yuhao Liang and Mohan Shi and Fan Yu and Yangze Li and Shiliang Zhang and Zhihao Du and Qian Chen and Lei Xie and Yanmin Qian and Jian Wu and Zhuo Chen and Kong Aik Lee and Zhijie Yan and Hui Bu},
journal= {arXiv preprint arXiv:2309.13573},
year = {2023}
}
Comments
8 pages, Accepted by ASRU2023