20 citations · 24 across the 10 of their papers we have counts for
10 papers
Exploring Speaker Diarization with Mixture of Experts
Gaobin Yang, Maokui He, Shutong Niu +3
In this paper, we propose a novel neural speaker diarization system using memory-aware multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S), which integrates a…
Quality-Aware End-to-End Audio-Visual Neural Speaker Diarization
Mao-Kui He, Jun Du, Shu-Tong Niu +2
In this paper, we propose a quality-aware end-to-end audio-visual neural speaker diarization framework, which comprises three key techniques. First, our audio-visual model takes bo…
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…
The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition
Zhe Wang, Shilong Wu, Hang Chen +12
The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research…
The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party meeting transcription (M2MeT) challenge
Maokui He, Xiang Lv, Weilin Zhou +8
We propose two improvements to target-speaker voice activity detection (TS-VAD), the core component in our proposed speaker diarization system that was submitted to the 2022 Multi-…
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker
Maokui He, Desh Raj, Zili Huang +3
Target-speaker voice activity detection (TS-VAD) has recently shown promising results for speaker diarization on highly overlapped speech. However, the original model requires a fi…
USTC-NELSLIP System Description for DIHARD-III Challenge
Yuxuan Wang, Maokui He, Shutong Niu +6
This system description describes our submission system to the Third DIHARD Speech Diarization Challenge. Besides the traditional clustering based system, the innovation of our sys…
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Desh Raj, Pavel Denisov, Zhuo Chen +11
Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in syst…