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20202022
most citedDisentangled speaker and nuisance attribute embedding for robust speaker verification

15 citations · 21 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2022

Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification

Sung Hwan Mun, Jee-weon Jung, Min Hyun Han +1

The majority of recent state-of-the-art speaker verification architectures adopt multi-scale processing and frequency-channel attention mechanisms. Convolutional layers of these mo…

eess.AS2021★ 5 cited

Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification

Sung Hwan Mun, Min Hyun Han, Dongjune Lee +2

In this paper, we propose self-supervised speaker representation learning strategies, which comprise of a bootstrap equilibrium speaker representation learning in the front-end and…

eess.AS2020★ 1 cited

Unsupervised Representation Learning for Speaker Recognition via Contrastive Equilibrium Learning

Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han +1

In this paper, we propose a simple but powerful unsupervised learning method for speaker recognition, namely Contrastive Equilibrium Learning (CEL), which increases the uncertainty…

eess.AS2020

Robust Text-Dependent Speaker Verification via Character-Level Information Preservation for the SdSV Challenge 2020

Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han +1

This paper describes our submission to Task 1 of the Short-duration Speaker Verification (SdSV) challenge 2020. Task 1 is a text-dependent speaker verification task, where both the…

eess.AS2020★ 15 cited

Disentangled speaker and nuisance attribute embedding for robust speaker verification

Woo Hyun Kang, Sung Hwan Mun, Min Hyun Han +1

Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the cl…