399 citations · 591 across the 32 of their papers we have counts for
40 papers
Toward accurate speaker inventories for online speaker diarization
Youngki Kwon, Hee-Soo Heo, Minjae Lee +1
An online speaker diarizer maintains a growing inventory of speakers, and applications read it directly: a live transcript enumerates them as they appear. Existing systems either f…
Using the enrollment gallery as evidence: affine-invariant score calibration for target speaker tagging
Hee-Soo Heo, Minjae Lee, Youngki Kwon +1
Target speaker tagging (TST) assigns enrolled speaker identities to diarized segments of multi-speaker recordings. The enrollment utterances of the other registered speakers are an…
Who Spoke When in Multi-Conversation: Target Speaker Tagging Task and Benchmark
Minjae Lee, Hee-Soo Heo, Youngki Kwon +3
We present target speaker tagging (TST), a task that integrates speaker diarization, verification, and identification into a unified workflow for multi-speaker conversations. Given…
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…
Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification
Hee-Soo Heo, KiHyun Nam, Bong-Jin Lee +4
In the field of speaker verification, session or channel variability poses a significant challenge. While many contemporary methods aim to disentangle session information from spea…
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…
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…
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…
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…
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
Jee-weon Jung, Hee-Soo Heo, Hemlata Tak +5
Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains. Their reliable detection usually depends upon computationally demanding e…
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity
You Jin Kim, Hee-Soo Heo, Jee-weon Jung +3
The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation sys…
Look Who's Talking: Active Speaker Detection in the Wild
You Jin Kim, Hee-Soo Heo, Soyeon Choe +5
In this work, we present a novel audio-visual dataset for active speaker detection in the wild. A speaker is considered active when his or her face is visible and the voice is audi…
Adapting Speaker Embeddings for Speaker Diarisation
Youngki Kwon, Jee-weon Jung, Hee-Soo Heo +3
The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation. The quality of speaker embeddings is paramount to the performance of speaker d…
Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network
Jee-weon Jung, Hee-Soo Heo, Youngki Kwon +2
In this work, we propose an overlapped speech detection system trained as a three-class classifier. Unlike conventional systems that perform binary classification as to whether or…
Look who's not talking
Youngki Kwon, Hee Soo Heo, Jaesung Huh +2
The objective of this work is speaker diarisation of speech recordings 'in the wild'. The ability to determine speech segments is a crucial part of diarisation systems, accounting…
Learning in your voice: Non-parallel voice conversion based on speaker consistency loss
Yoohwan Kwon, Soo-Whan Chung, Hee-Soo Heo +1
In this paper, we propose a novel voice conversion strategy to resolve the mismatch between the training and conversion scenarios when parallel speech corpus is unavailable for tra…