# Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge

arXiv: https://arxiv.org/abs/2202.03647 · HTML: https://mlsanity.com/paper/2202.03647/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2022.

## Built on by (most cited first, 11 of 11)

- [SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models](https://mlsanity.com/paper/2508.06372/) (2025)
- [StoryTeller: Improving Long Video Description through Global Audio-Visual Character Identification](https://mlsanity.com/paper/2411.07076/) (2024)
- [One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition](https://mlsanity.com/paper/2310.01688/) (2023)
- [A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings](https://mlsanity.com/paper/2203.16834/) (2022)
- [BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR](https://mlsanity.com/paper/2305.13716/) (2023)
- [DiariST: Streaming Speech Translation with Speaker Diarization](https://mlsanity.com/paper/2309.08007/) (2023)
- [Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTC](https://mlsanity.com/paper/2409.12388/) (2024)
- [Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios](https://mlsanity.com/paper/2203.09767/) (2022)
- [FADE: A Dataset for Detecting Falling Objects around Buildings in Video](https://mlsanity.com/paper/2408.05750/) (2024)
- [Xiaomi-CocktailASR-1 Technical Report](https://mlsanity.com/paper/2609.11274/) (2026)
- [Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding](https://mlsanity.com/paper/2608.24048/) (2026)

## 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.