41 citations · 67 across the 13 of their papers we have counts for
8 papers · 1 filter
Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation
Ji Xu, Yuan Xie, Wenchao Wang
Underwater acoustic target recognition is a challenging task owing to the intricate underwater environments and limited data availability. Insufficient data can hinder the ability…
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification
Zhuo Li, Jingze Lu, Zhenduo Zhao +2
Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker…
The HCCL system for VoxCeleb Speaker Recognition Challenge 2022
Zhenduo Zhao, Zhuo Li, Wenchao Wang +1
This report describes our submission to track1 and track3 for VoxCeleb Speaker Recognition Challenge 2022(VoxSRC2022). Our best system achieves minDCF 0.1397 and EER 2.414 in track…
PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification
Zhenduo Zhao, Zhuo Li, Wenchao Wang +1
ECAPA-TDNN is currently the most popular TDNN-series model for speaker verification, which refreshed the state-of-the-art(SOTA) performance of TDNN models. However, one-dimensional…
The HCCL System for the NIST SRE21
Zhuo Li, Runqiu Xiao, Hangting Chen +3
This paper describes the systems developed by the HCCL team for the NIST 2021 speaker recognition evaluation (NIST SRE21).We first explore various state-of-the-art speaker embeddin…
Back-ends Selection for Deep Speaker Embeddings
Zhuo Li, Runqiu Xiao, Zihan Zhang +3
Probabilistic Linear Discriminant Analysis (PLDA) was the dominant and necessary back-end for early speaker recognition approaches, like i-vector and x-vector. However, with the de…
The HCCL Speaker Verification System for Far-Field Speaker Verification Challenge
Zhuo Li, Ce Fang, Runqiu Xiao +3
This paper describes the systems submitted by team HCCL to the Far-Field Speaker Verification Challenge. Our previous work in the AIshell Speaker Verification Challenge 2019 shows…
Adaptive Margin Circle Loss for Speaker Verification
Runqiu Xiao
Deep-Neural-Network (DNN) based speaker verification sys-tems use the angular softmax loss with margin penalties toenhance the intra-class compactness of speaker embeddings,which a…