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20182023
most citedDisentangled speaker and nuisance attribute embedding for robust speaker verification

15 citations · 36 across the 8 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

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.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…

cs.SD2020★ 7 cited

WaveNODE: A Continuous Normalizing Flow for Speech Synthesis

Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang +3

In recent years, various flow-based generative models have been proposed to generate high-fidelity waveforms in real-time. However, these models require either a well-trained teach…

eess.AS2020

Gated Recurrent Context: Softmax-free Attention for Online Encoder-Decoder Speech Recognition

Hyeonseung Lee, Woo Hyun Kang, Sung Jun Cheon +2

Recently, attention-based encoder-decoder (AED) models have shown state-of-the-art performance in automatic speech recognition (ASR). As the original AED models with global attenti…

cs.CV2020

SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds

Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang +2

Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. Therefore, flow-based models cannot be adequately traine…