most citedSelf-Supervised Learning Based Domain Adaptation for Robust Speaker Verification

41 citations · 59 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD20221 cited

A comprehensive study on self-supervised distillation for speaker representation learning

Zhengyang Chen, Yao Qian, Bing Han +2

In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised…

cs.SD20223 cited

Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit

Hongji Wang, Chengdong Liang, Shuai Wang +5

Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation…

cs.SD20224 cited

SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022

Zhengyang Chen, Bing Han, Xu Xiang +3

This report describes the SJTU-AISPEECH system for the Voxceleb Speaker Recognition Challenge 2022. For track1, we implemented two kinds of systems, the online system and the offli…

cs.CL202110 cited

UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training

Sanyuan Chen, Yu Wu, Chengyi Wang +8

Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness…

cs.SD202141 cited

Self-Supervised Learning Based Domain Adaptation for Robust Speaker Verification

Zhengyang Chen, Shuai Wang, Yanmin Qian

Large performance degradation is often observed for speaker ver-ification systems when applied to a new domain dataset. Givenan unlabeled target-domain dataset, unsupervised domain…