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arXiv · 2209.12002

Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting

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

This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting. The diarization system obtains direction information of speaker by microphone array. Speaker spatial embedding is generated by xvector and s-vector derived from superdirective beamforming (SDB) 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 superdirective beamforming (ASDB) block and Conformer encoder. The proposed ASDB is a self-adapted channel-wise block that extracts the latent spatial features of array audios by modeling interdependencies between channels. We explore DMSNet to address overlapped speech problem on multi-channel audio and achieve 93.53% accuracy on evaluation set. By performing DMSNet based overlapped speech detection (OSD) module, the diarization error rate (DER) of cluster-based diarization system decrease significantly from 13.45% to 7.64%.

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Jie Wang, Yuji Liu, Binling Wang, Yiming Zhi, Song Li, Shipeng Xia, Jiayang Zhang, Feng Tong, Lin Li, Qingyang Hong. 2022-09-24. Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting. https://doi.org/10.21437/interspeech.2022-11412

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