# MFCCA:Multi-Frame Cross-Channel attention for multi-speaker ASR in Multi-party meeting scenario

arXiv: https://arxiv.org/abs/2210.05265 · HTML: https://mlsanity.com/paper/2210.05265/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR](https://mlsanity.com/paper/2305.13716/) (2023)
- [End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder and Input Feature Analysis](https://mlsanity.com/paper/2310.10106/) (2023)
- [Joint Beamforming and Speaker-Attributed ASR for Real Distant-Microphone Meeting Transcription](https://mlsanity.com/paper/2410.21849/) (2024)
- [SpatialEmb: Extract and Encode Spatial Information for 1-Stage Multi-channel Multi-speaker ASR on Arbitrary Microphone Arrays](https://mlsanity.com/paper/2601.18037/) (2026)

## Abstract (arXiv)

Recently cross-channel attention, which better leverages multi-channel signals from microphone array, has shown promising results in the multi-party meeting scenario. Cross-channel attention focuses on either learning global correlations between sequences of different channels or exploiting fine-grained channel-wise information effectively at each time step. Considering the delay of microphone array receiving sound, we propose a multi-frame cross-channel attention, which models cross-channel information between adjacent frames to exploit the complementarity of both frame-wise and channel-wise knowledge. Besides, we also propose a multi-layer convolutional mechanism to fuse the multi-channel output and a channel masking strategy to combat the channel number mismatch problem between training and inference. Experiments on the AliMeeting, a real-world corpus, reveal that our proposed model outperforms single-channel model by 31.7% and 37.0% CER reduction on Eval and Test sets. Moreover, with comparable model parameters and training data, our proposed model achieves a new SOTA performance on the AliMeeting corpus, as compared with the top ranking systems in the ICASSP2022 M2MeT challenge, a recently held multi-channel multi-speaker ASR challenge.