Search Results for author: Sung Hwan Mun

Found 9 papers, 3 papers with code

EEND-DEMUX: End-to-End Neural Speaker Diarization via Demultiplexed Speaker Embeddings

no code implementations11 Dec 2023 Sung Hwan Mun, Min Hyun Han, Canyeong Moon, Nam Soo Kim

In recent years, there have been studies to further improve the end-to-end neural speaker diarization (EEND) systems.

speaker-diarization Speaker Diarization

Towards single integrated spoofing-aware speaker verification embeddings

1 code implementation30 May 2023 Sung Hwan Mun, Hye-jin Shim, Hemlata Tak, Xin Wang, Xuechen Liu, Md Sahidullah, Myeonghun Jeong, Min Hyun Han, Massimiliano Todisco, Kong Aik Lee, Junichi Yamagishi, Nicholas Evans, Tomi Kinnunen, Nam Soo Kim, Jee-weon Jung

Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge.

Speaker Verification

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

no code implementations12 Oct 2022 Byoung Jin Choi, Myeonghun Jeong, Minchan Kim, Sung Hwan Mun, Nam Soo Kim

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 with a set of pre-defined speakers.

Fully Unsupervised Training of Few-shot Keyword Spotting

no code implementations6 Oct 2022 Dongjune Lee, Minchan Kim, Sung Hwan Mun, Min Hyun Han, Nam Soo Kim

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 keywords with only a few enrollment samples.

Keyword Spotting Metric Learning +1

Disentangled Speaker Representation Learning via Mutual Information Minimization

no code implementations17 Aug 2022 Sung Hwan Mun, Min Hyun Han, Minchan Kim, Dongjune Lee, Nam Soo Kim

The experimental results show that fine-tuning with a disentanglement framework on a existing pre-trained model is valid and can further improve performance.

Disentanglement Speaker Recognition +2

Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification

1 code implementation3 Apr 2022 Sung Hwan Mun, Jee-weon Jung, Min Hyun Han, Nam Soo Kim

The SKA mechanism allows each convolutional layer to adaptively select the kernel size in a data-driven fashion.

Speaker Verification

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

no code implementations16 Dec 2021 Sung Hwan Mun, Min Hyun Han, Dongjune Lee, JiHwan Kim, Nam Soo Kim

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 an uncertainty-aware probabilistic speaker embedding training in the back-end.

Contrastive Learning Representation Learning +1

Unsupervised Representation Learning for Speaker Recognition via Contrastive Equilibrium Learning

1 code implementation22 Oct 2020 Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han, Nam Soo Kim

In this paper, we propose a simple but powerful unsupervised learning method for speaker recognition, namely Contrastive Equilibrium Learning (CEL), which increases the uncertainty on nuisance factors latent in the embeddings by employing the uniformity loss.

Representation Learning Speaker Recognition +1

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

no code implementations22 Oct 2020 Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han, Nam Soo Kim

This paper describes our submission to Task 1 of the Short-duration Speaker Verification (SdSV) challenge 2020.

Audio and Speech Processing Sound

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