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arXiv · 2601.02688

Multi-channel multi-speaker transformer for speech recognition

Guo Yifan·Tian Yao·Suo Hongbin·Wan Yulong
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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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Guo Yifan, Tian Yao, Suo Hongbin, Wan Yulong. 2026-01-06. Multi-channel multi-speaker transformer for speech recognition. https://doi.org/10.21437/interspeech.2023-257

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