most citedDisentangled speaker and nuisance attribute embedding for robust speaker verification

15 citations · 15 across the 3 of their papers we have counts for

collaborators

5 papers

eess.AS2022

Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech

Byoung Jin Choi, Myeonghun Jeong, Minchan Kim +2

Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios wit…

eess.AS2022

Fully Unsupervised Training of Few-shot Keyword Spotting

Dongjune Lee, Minchan Kim, Sung Hwan Mun +2

For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keyw…

eess.AS2020

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.AS202015 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…