b'Hye-Jin Shim'

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research

∙ 05/31/2023

How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning

Shortcut learning, or `Clever Hans effect` refers to situations where a ...

0 Hye-Jin Shim, et al. ∙

research

∙ 05/31/2023

Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing

Audio anti-spoofing for automatic speaker verification aims to safeguard...

0 Hye-Jin Shim, et al. ∙

research

∙ 05/30/2023

Towards single integrated spoofing-aware speaker verification embeddings

This study aims to develop a single integrated spoofing-aware speaker ve...

2 Sung Hwan Mun, et al. ∙

research

∙ 10/20/2022

Large-scale learning of generalised representations for speaker recognition

The objective of this work is to develop a speaker recognition model to ...

0 Jee-weon Jung, et al. ∙

research

∙ 06/27/2022

Extended U-Net for Speaker Verification in Noisy Environments

Background noise is a well-known factor that deteriorates the accuracy a...

0 Ju-ho Kim, et al. ∙

research

∙ 04/21/2022

Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion

Deep learning has brought impressive progress in the study of both autom...

0 Hye-Jin Shim, et al. ∙

research

∙ 01/25/2022

SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan

ASV (automatic speaker verification) systems are intrinsically required ...

0 Jee-weon Jung, et al. ∙

research

∙ 12/23/2021

Graph attentive feature aggregation for text-independent speaker verification

The objective of this paper is to combine multiple frame-level features ...

0 Hye-Jin Shim, et al. ∙

research

∙ 10/04/2021

AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks

Artefacts that differentiate spoofed from bona-fide utterances can resid...

0 Jee-weon Jung, et al. ∙

research

∙ 04/15/2021

Attentive Max Feature Map for Acoustic Scene Classification with Joint Learning considering the Abstraction of Classes

The attention mechanism has been widely adopted in acoustic scene classi...

1 Hye-Jin Shim, et al. ∙

research

∙ 04/14/2021

Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System

Most speaker verification tasks are studied as an open-set evaluation sc...

0 Ju-ho Kim, et al. ∙

research

∙ 07/09/2020

Capturing scattered discriminative information using a deep architecture in acoustic scene classification

Frequently misclassified pairs of classes that share many common acousti...

0 Hye-Jin Shim, et al. ∙

research

∙ 06/10/2020

Integrated Replay Spoofing-aware Text-independent Speaker Verification

A number of studies have successfully developed speaker verification or ...

0 Hye-Jin Shim, et al. ∙

research

∙ 05/07/2020

Segment Aggregation for short utterances speaker verification using raw waveforms

Most studies on speaker verification systems focus on long-duration utte...

0 Seung-bin Kim, et al. ∙

research

∙ 05/07/2020

Crop Aggregating for short utterances speaker verification using raw waveforms

Most studies on speaker verification systems focus on long-duration utte...

0 Seung-bin Kim, et al. ∙

research

∙ 04/01/2020

Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms

Recent advances in deep learning have facilitated the design of speaker ...

0 Jee-weon Jung, et al. ∙

research

∙ 04/01/2020

Improved RawNet with Filter-wise Rescaling for Text-independent Speaker Verification using Raw Waveforms

Recent advances in deep learning have facilitated the design of speaker ...

0 Jee-weon Jung, et al. ∙

research

∙ 01/31/2020

A study on the role of subsidiary information in replay attack spoofing detection

In this study, we analyze the role of various categories of subsidiary i...

0 Jee-weon Jung, et al. ∙

research

∙ 10/22/2019

Self-supervised pre-training with acoustic configurations for replay spoofing detection

Large datasets are well-known as a key to the recent advances in deep le...

0 Hye-Jin Shim, et al. ∙

research

∙ 07/01/2019

Cosine similarity-based adversarial process

An adversarial process between two deep neural networks is a promising a...

0 Hee-Soo Heo, et al. ∙

research

∙ 04/23/2019

Acoustic scene classification using teacher-student learning with soft-labels

Acoustic scene classification identifies an input segment into one of th...

0 Hee-Soo Heo, et al. ∙

research

∙ 04/23/2019

Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge

In this study, we concentrate on replacing the process of extracting han...

0 Jee-weon Jung, et al. ∙

research

∙ 04/17/2019

RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification

Recently, direct modeling of raw waveforms using deep neural networks ha...

0 Jee-weon Jung, et al. ∙

research

∙ 02/07/2019

End-to-end losses based on speaker basis vectors and all-speaker hard negative mining for speaker verification

In recent years, speaker verification has been primarily performed using...

0 Hee-Soo Heo, et al. ∙

research

∙ 10/25/2018

Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings

Input utterance with short duration is one of the most critical threats ...

0 Jee-weon Jung, et al. ∙

research

∙ 08/29/2018

Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes

In this paper, we propose a replay attack spoofing detection system for ...

0 Hye-Jin Shim, et al. ∙

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