Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge
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Abstract (arXiv)
The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologies. The M2MeT challenge has particularly set up two tracks, speaker diarization (track 1) and multi-speaker automatic speech recognition (ASR) (track 2). Along with the challenge, we released 120 hours of real-recorded Mandarin meeting speech data with manual annotation, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants' headset microphone. We briefly describe the released dataset, track setups, baselines and summarize the challenge results and major techniques used in the submissions.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2022.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 1; 2026: 2.
11 papers.
- 2025SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Modelscited by 14
- 2024StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identificationcited by 13
- 2023One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognitioncited by 12
- 2022A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetingscited by 5
- 2023BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASRcited by 5
- 2023DiariST: Streaming Speech Translation with Speaker Diarizationcited by 5
- 2024Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTCcited by 4
- 2022Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarioscited by 2
- 2024FADE: A Dataset for Detecting Falling Objects around Buildings in Videocited by 1
- 2026Xiaomi-CocktailASR-1 Technical Reportcited by 1
- 2026Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understandingcited by 0
- 2026Xiaomi-CocktailASR-1 Technical Report10 Sep 2026
- 2026Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding25 Aug 2026
- 2025SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models8 Aug 2025
- 2024StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identification11 Nov 2024
- 2024Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTC19 Sep 2024
- 2024FADE: A Dataset for Detecting Falling Objects around Buildings in Video11 Aug 2024
- 2023One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition2 Oct 2023
- 2023DiariST: Streaming Speech Translation with Speaker Diarization14 Sep 2023
- 2023BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR23 May 2023
- 2022A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings31 Mar 2022
- 2022Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios18 Mar 2022