1 citations · 1 across the 3 of their papers we have counts for
3 papers
eess.AS2025
M3SD: Multi-modal, Multi-scenario and Multi-language Speaker Diarization Dataset
Shilong Wu
In the field of speaker diarization, the development of technology is constrained by two problems: insufficient data resources and poor generalization ability of deep learning mode…
eess.AS2023
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge
Ruoyu Wang, Maokui He, Jun Du +16
This technical report details our submission system to the CHiME-7 DASR Challenge, which focuses on speaker diarization and speech recognition under complex multi-speaker scenarios…
eess.AS2023★ 1 cited
Semi-supervised multi-channel speaker diarization with cross-channel attention
Shilong Wu, Jun Du, Maokui He +4
Most neural speaker diarization systems rely on sufficient manual training data labels, which are hard to collect under real-world scenarios. This paper proposes a semi-supervised…