TY - GEN
T1 - The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0)
T2 - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
AU - Liang, Yuhao
AU - Shi, Mohan
AU - Yu, Fan
AU - Li, Yangze
AU - Zhang, Shiliang
AU - Du, Zhihao
AU - Chen, Qian
AU - Xie, Lei
AU - Qian, Yanmin
AU - Wu, Jian
AU - Chen, Zhuo
AU - Lee, Kong Aik
AU - Yan, Zhijie
AU - Bu, Hui
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 speaker-attributed ASR (SAASR), 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.
AB - 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 speaker-attributed ASR (SAASR), 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.
KW - Alimeeting
KW - M2MeT 2.0
KW - Meeting Transcription
KW - Multi-speaker ASR
KW - Speaker-attributed ASR
UR - http://www.scopus.com/inward/record.url?scp=85184667150&partnerID=8YFLogxK
U2 - 10.1109/ASRU57964.2023.10389625
DO - 10.1109/ASRU57964.2023.10389625
M3 - 会议稿件
AN - SCOPUS:85184667150
T3 - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
BT - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 December 2023 through 20 December 2023
ER -