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20182021
most citedDisentangled speaker and nuisance attribute embedding for robust speaker verification

15 citations · 31 across the 4 of their papers we have counts for

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eess.AS20218 cited

Diff-TTS: A Denoising Diffusion Model for Text-to-Speech

Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon +2

Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness…

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…

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…