16 citations · 133 across the 45 of their papers we have counts for
48 papers
Token-based Attractors and Cross-attention in Spoof Diarization
Kyo-Won Koo, Chan-yeong Lim, Jee-weon Jung +2
Spoof diarization identifies ``what spoofed when" in a given speech by temporally locating spoofed regions and determining their manipulation techniques. As a first step toward thi…
WildSpoof Challenge Evaluation Plan
Yihan Wu, Jee-weon Jung, Hye-jin Shim +2
The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthes…
SEED: Speaker Embedding Enhancement Diffusion Model
KiHyun Nam, Jungwoo Heo, Jee-weon Jung +4
A primary challenge when deploying speaker recognition systems in real-world applications is performance degradation caused by environmental mismatch. We propose a diffusion-based…
Disentangled Representation Learning for Environment-agnostic Speaker Recognition
KiHyun Nam, Hee-Soo Heo, Jee-weon Jung +1
This work presents a framework based on feature disentanglement to learn speaker embeddings that are robust to environmental variations. Our framework utilises an auto-encoder as a…
To what extent can ASV systems naturally defend against spoofing attacks?
Jee-weon Jung, Xin Wang, Nicholas Evans +6
The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target. However, emerging advancements in speech gener…
a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
Hye-jin Shim, Jee-weon Jung, Tomi Kinnunen +3
Spoofing detection is today a mainstream research topic. Standard metrics can be applied to evaluate the performance of isolated spoofing detection solutions and others have been p…
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Yifan Peng, Jinchuan Tian, Brian Yan +13
Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…
Encoder-decoder multimodal speaker change detection
Jee-weon Jung, Soonshin Seo, Hee-Soo Heo +5
The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications. Several studies solved the SCD task using…
Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing
Hye-jin Shim, Jee-weon Jung, Tomi Kinnunen
Audio anti-spoofing for automatic speaker verification aims to safeguard users' identities from spoofing attacks. Although state-of-the-art spoofing countermeasure(CM) models perfo…
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge
Jaesung Huh, Andrew Brown, Jee-weon Jung +4
This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge…
Absolute decision corrupts absolutely: conservative online speaker diarisation
Youngki Kwon, Hee-Soo Heo, Bong-Jin Lee +2
Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated…
High-resolution embedding extractor for speaker diarisation
Hee-Soo Heo, Youngki Kwon, Bong-Jin Lee +2
Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each spee…
Disentangled representation learning for multilingual speaker recognition
Kihyun Nam, Youkyum Kim, Jaesung Huh +3
The goal of this paper is to learn robust speaker representation for bilingual speaking scenario. The majority of the world's population speak at least two languages; however, most…
Large-scale learning of generalised representations for speaker recognition
Jee-weon Jung, Hee-Soo Heo, Bong-Jin Lee +5
The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two components should be adequately configured to build su…
In search of strong embedding extractors for speaker diarisation
Jee-weon Jung, Hee-Soo Heo, Bong-Jin Lee +5
Speaker embedding extractors (EEs), which map input audio to a speaker discriminant latent space, are of paramount importance in speaker diarisation. However, there are several cha…
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
Hye-jin Shim, Hemlata Tak, Xuechen Liu +12
Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent…
Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification
Sung Hwan Mun, Jee-weon Jung, Min Hyun Han +1
The majority of recent state-of-the-art speaker verification architectures adopt multi-scale processing and frequency-channel attention mechanisms. Convolutional layers of these mo…
Pushing the limits of raw waveform speaker recognition
Jee-weon Jung, You Jin Kim, Hee-Soo Heo +3
In recent years, speaker recognition systems based on raw waveform inputs have received increasing attention. However, the performance of such systems are typically inferior to the…
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +6
The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scen…
SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +7
ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or conve…
Curriculum learning for self-supervised speaker verification
Hee-Soo Heo, Jee-weon Jung, Jingu Kang +4
The goal of this paper is to train effective self-supervised speaker representations without identity labels. We propose two curriculum learning strategies within a self-supervised…
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation
Hemlata Tak, Massimiliano Todisco, Xin Wang +3
The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data. With this usually being limited, current solutio…
Multi-scale speaker embedding-based graph attention networks for speaker diarisation
Youngki Kwon, Hee-Soo Heo, Jee-weon Jung +3
The objective of this work is effective speaker diarisation using multi-scale speaker embeddings. Typically, there is a trade-off between the ability to recognise short speaker seg…