1 code implementation • 16 Jul 2020 • Hu Hu, Chao-Han Huck Yang, Xianjun Xia, Xue Bai, Xin Tang, Yajian Wang, Shutong Niu, Li Chai, Juanjuan Li, Hongning Zhu, Feng Bao, Yuanjun Zhao, Sabato Marco Siniscalchi, Yannan Wang, Jun Du, Chin-Hui Lee
On Task 1b development data set, we achieve an accuracy of 96. 7\% with a model size smaller than 500KB.
1 code implementation • 3 Nov 2020 • Hu Hu, Chao-Han Huck Yang, Xianjun Xia, Xue Bai, Xin Tang, Yajian Wang, Shutong Niu, Li Chai, Juanjuan Li, Hongning Zhu, Feng Bao, Yuanjun Zhao, Sabato Marco Siniscalchi, Yannan Wang, Jun Du, Chin-Hui Lee
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.
Ranked #1 on Acoustic Scene Classification on TAU Urban Acoustic Scenes 2019 (using extra training data)
no code implementations • 19 Mar 2021 • Yuxuan Wang, Maokui He, Shutong Niu, Lei Sun, Tian Gao, Xin Fang, Jia Pan, Jun Du, Chin-Hui Lee
This system description describes our submission system to the Third DIHARD Speech Diarization Challenge.
no code implementations • 10 Feb 2022 • Maokui He, Xiang Lv, Weilin Zhou, JingJing Yin, Xiaoqi Zhang, Yuxuan Wang, Shutong Niu, Yuhang Cao, Heng Lu, Jun Du, Chin-Hui Lee
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-Channel Multi-Party Meeting Transcription (M2MeT) challenge.
no code implementations • 17 Jul 2023 • Shilong Wu, Jun Du, Maokui He, Shutong Niu, Hang Chen, Haitao Tang, Chin-Hui Lee
Most neural speaker diarization systems rely on sufficient manual training data labels, which are hard to collect under real-world scenarios.
no code implementations • 28 Aug 2023 • Ruoyu Wang, Maokui He, Jun Du, Hengshun Zhou, Shutong Niu, Hang Chen, Yanyan Yue, Gaobin Yang, Shilong Wu, Lei Sun, Yanhui Tu, Haitao Tang, Shuangqing Qian, Tian Gao, Mengzhi Wang, Genshun Wan, Jia Pan, Jianqing Gao, Chin-Hui Lee
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.
1 code implementation • 17 Sep 2023 • Gaobin Yang, Maokui He, Shutong Niu, Ruoyu Wang, Yanyan Yue, Shuangqing Qian, Shilong Wu, Jun Du, Chin-Hui Lee
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 memory-aware multi-speaker embedding (MA-MSE) and sequence-to-sequence (Seq2Seq) architecture, leading to improvement in both efficiency and performance.