Multi-channel multi-speaker transformer for speech recognition

• URL: http://arxiv.org/abs/2601.02688v1
• Date: Tue, 06 Jan 2026 03:46:07 GMT
• Title: Multi-channel multi-speaker transformer for speech recognition
• Authors: Guo Yifan, Tian Yao, Suo Hongbin, Wan Yulong,
• Abstract summary: We propose the multi-channel multi-speaker transformer (M2Former) for far-field multi-speaker ASR.<n>M2Former outperforms neural beamformer, MCT, dual-path RNN with transform-average-concatenate and multi-channel deep clustering based end-to-end systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of teleconferencing and in-vehicle voice assistants, far-field multi-speaker speech recognition has become a hot research topic. Recently, a multi-channel transformer (MCT) has been proposed, which demonstrates the ability of the transformer to model far-field acoustic environments. However, MCT cannot encode high-dimensional acoustic features for each speaker from mixed input audio because of the interference between speakers. Based on these, we propose the multi-channel multi-speaker transformer (M2Former) for far-field multi-speaker ASR in this paper. Experiments on the SMS-WSJ benchmark show that the M2Former outperforms the neural beamformer, MCT, dual-path RNN with transform-average-concatenate and multi-channel deep clustering based end-to-end systems by 9.2%, 14.3%, 24.9%, and 52.2% respectively, in terms of relative word error rate reduction.

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