activity
20172022
most citedLarge Margin Softmax Loss for Speaker Verification

17 citations · 18 across the 6 of their papers we have counts for

collaborators

11 papers

eess.AS2022

I4U System Description for NIST SRE'20 CTS Challenge

Kong Aik Lee, Tomi Kinnunen, Daniele Colibro +23

This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resu…

cs.SD2019

THUEE system description for NIST 2019 SRE CTS Challenge

Yi Liu, Tianyu Liang, Can Xu +9

This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the…

eess.AS2019

Adaptive Multi-scale Detection of Acoustic Events

Wenhao Ding, Liang He

The goal of acoustic (or sound) events detection (AED or SED) is to predict the temporal position of target events in given audio segments. This task plays a significant role in sa…

eess.AS2019

Latent Class Model with Application to Speaker Diarization

Liang He, Xianhong Chen, Can Xu +3

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft informat…

cs.SD201917 cited

Large Margin Softmax Loss for Speaker Verification

Yi Liu, Liang He, Jia Liu

In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small. A variety of…

cs.SD2019

Multi-Scale Time-Frequency Attention for Acoustic Event Detection

Jingyang Zhang, Wenhao Ding, Jintao Kang +1

Most attention-based methods only concentrate along the time axis, which is insufficient for Acoustic Event Detection (AED). Meanwhile, previous methods for AED rarely considered t…