The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR

Yuhao Liang; Mohan Shi; Fan Yu; Yangze Li; Shiliang Zhang; Zhihao Du; Qian Chen; Lei Xie; Yanmin Qian; Jian Wu; Zhuo Chen; Kong Aik Lee; Zhijie Yan; Hui Bu

doi:10.1109/ASRU57964.2023.10389625

The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR

Yuhao Liang, Mohan Shi, Fan Yu, Yangze Li, Shiliang Zhang, Zhihao Du, Qian Chen, Lei Xie, Yanmin Qian, Jian Wu, Zhuo Chen, Kong Aik Lee, Zhijie Yan, Hui Bu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

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 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.

Original language	English
Title of host publication	2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350306897
DOIs	https://doi.org/10.1109/ASRU57964.2023.10389625
Publication status	Published - Dec 2023
Externally published	Yes
Event	2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023 - Taipei, Taiwan Duration: 16 Dec 2023 → 20 Dec 2023

Publication series

Name	2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

Conference

Conference	2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023
Country/Territory	Taiwan
City	Taipei
Period	16/12/23 → 20/12/23

Keywords

Alimeeting
M2MeT 2.0
Meeting Transcription
Multi-speaker ASR
Speaker-attributed ASR

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Signal Processing
Acoustics and Ultrasonics
Linguistics and Language
Communication

More information

10.1109/ASRU57964.2023.10389625

Cite this

Liang, Y., Shi, M., Yu, F., Li, Y., Zhang, S., Du, Z., Chen, Q., Xie, L., Qian, Y., Wu, J., Chen, Z., Lee, K. A., Yan, Z., & Bu, H. (2023). The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR. In 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023 Article 10389625 (2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASRU57964.2023.10389625

Liang, Yuhao ; Shi, Mohan ; Yu, Fan et al. / The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR. 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023).

@inproceedings{17b98602cb454f2c8f20a1d4ef24ae69,

title = "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 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.",

keywords = "Alimeeting, M2MeT 2.0, Meeting Transcription, Multi-speaker ASR, 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 Lee, {Kong Aik} and Zhijie Yan and Hui Bu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023 ; Conference date: 16-12-2023 Through 20-12-2023",

year = "2023",

month = dec,

doi = "10.1109/ASRU57964.2023.10389625",

language = "English",

series = "2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023",

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Liang, Y, Shi, M, Yu, F, Li, Y, Zhang, S, Du, Z, Chen, Q, Xie, L, Qian, Y, Wu, J, Chen, Z, Lee, KA, Yan, Z & Bu, H 2023, The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR. in 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023., 10389625, 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023, Taipei, Taiwan, 16/12/23. https://doi.org/10.1109/ASRU57964.2023.10389625

The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR. / Liang, Yuhao; Shi, Mohan; Yu, Fan et al.
2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 10389625 (2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Shi, Mohan

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AU - Li, Yangze

AU - Zhang, Shiliang

AU - Du, Zhihao

AU - Chen, Qian

AU - Xie, Lei

AU - Qian, Yanmin

AU - Wu, Jian

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AU - Lee, Kong Aik

AU - Yan, Zhijie

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BT - 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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Liang Y, Shi M, Yu F, Li Y, Zhang S, Du Z et al. The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR. In 2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023. Institute of Electrical and Electronics Engineers Inc. 2023. 10389625. (2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023). doi: 10.1109/ASRU57964.2023.10389625

The Second Multi-Channel Multi-Party Meeting Transcription Challenge (M2MeT 2.0): A Benchmark for Speaker-Attributed ASR

Abstract

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Keywords

ASJC Scopus subject areas

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