The xmuspeech system for multi-channel multi-party meeting transcription challenge

Jie Wang; Yuji Liu; Binling Wang; Yiming Zhi; Song Li1; Shipeng Xia; Jiayang Zhang; Lin Li1; Qingyang Hong; Feng Tong

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arXiv · 2202.05744

The xmuspeech system for multi-channel multi-party meeting transcription challenge

Jie Wang·Yuji Liu·Binling Wang·Yiming Zhi·Song Li1·Shipeng Xia·Jiayang Zhang·Lin Li1·Qingyang Hong·Feng Tong

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Abstract

This paper describes the system developed by the XMUSPEECH team for the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT). For the speaker diarization task, we propose a multi-channel speaker diarization system that obtains spatial information of speaker by Difference of Arrival (DOA) technology. Speaker-spatial embedding is generated by x-vector and s-vector derived from Filter-and-Sum Beamforming (FSB) which makes the embedding more robust. Specifically, we propose a novel multi-channel sequence-to-sequence neural network architecture named Discriminative Multi-stream Neural Network (DMSNet) which consists of Attention Filter-and-Sum block (AFSB) and Conformer encoder. We explore DMSNet to address overlapped speech problem on multi-channel audio. Compared with LSTM based OSD module, we achieve a decreases of 10.1% in Detection Error Rate(DetER). By performing DMSNet based OSD module, the DER of cluster-based diarization system decrease significantly form 13.44% to 7.63%. Our best fusion system achieves 7.09% and 9.80% of the diarization error rate (DER) on evaluation set and test set.

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Jie Wang, Yuji Liu, Binling Wang, Yiming Zhi, Song Li1, Shipeng Xia, Jiayang Zhang, Lin Li1, Qingyang Hong, Feng Tong. 2022-02-11. The xmuspeech system for multi-channel multi-party meeting transcription challenge. https://arxiv.org/abs/2202.05744

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