The second multi-channel multi-party meeting transcription challenge (M2MeT) 2.0): A benchmark for speaker-attributed ASR
| Autor: | Liang, Yuhao, Shi, Mohan, Yu, Fan, Li, Yangze, Zhang, Shiliang, Du, Zhihao, Chen, Qian, Xie, Lei, Qian, Yanmin, Wu, Jian, Chen, Zhuo, Lee, Kong Aik, Yan, Zhijie, Bu, Hui |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 8 pages, Accepted by ASRU2023 |
| Databáze: | arXiv |
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