activity
20202022
most citedThe DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge

19 citations · 41 across the 11 of their papers we have counts for

collaborators

11 papers

eess.AS2022

LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge

Yan Jia, Mi Hong, Jingyu Hou +7

This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without li…

eess.AS2021

Low-Latency Online Speaker Diarization with Graph-Based Label Generation

Yucong Zhang, Qinjian Lin, Weiqing Wang +4

This paper introduces an online speaker diarization system that can handle long-time audio with low latency. We enable Agglomerative Hierarchy Clustering (AHC) to work in an online…

cs.SD2021★ 1 cited

Towards Lightweight Applications: Asymmetric Enroll-Verify Structure for Speaker Verification

Qingjian Lin, Lin Yang, Xuyang Wang +3

With the development of deep learning, automatic speaker verification has made considerable progress over the past few years. However, to design a lightweight and robust system wit…

eess.AS2021★ 19 cited

The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge

Weiqing Wang, Danwei Cai, Qingjian Lin +4

This report describes the submission of the DKU-DukeECE-Lenovo team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2021 track 4. Our system including a voice activity detec…

cs.SD2021★ 7 cited

Sparsely Overlapped Speech Training in the Time Domain: Joint Learning of Target Speech Separation and Personal VAD Benefits

Qingjian Lin, Lin Yang, Xuyang Wang +3

Target speech separation is the process of filtering a certain speaker's voice out of speech mixtures according to the additional speaker identity information provided. Recent work…

cs.CL2021

TransfoRNN: Capturing the Sequential Information in Self-Attention Representations for Language Modeling

Tze Yuang Chong, Xuyang Wang, Lin Yang +1

In this paper, we describe the use of recurrent neural networks to capture sequential information from the self-attention representations to improve the Transformers. Although self…