1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition
Ming Gao, Shilong Wu, Hang Chen +6
Meetings are a valuable yet challenging scenario for speech applications due to complex acoustic conditions. This paper summarizes the outcomes of the MISP 2025 Challenge, hosted a…
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
Shilong Wu, Chenxi Wang, Hang Chen +13
Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advan…
Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture
Gaobin Yang, Maokui He, Shutong Niu +6
We propose a novel neural speaker diarization system using memory-aware multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S), which integrates the strengths of…
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…
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…