activity
20182026
most citedIn defence of metric learning for speaker recognition

399 citations · 591 across the 32 of their papers we have counts for

collaborators
Showing 2022Show all

10 papers · 1 filter

eess.AS2022

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…

cs.SD2022★ 1 cited

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…

eess.AS2022

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…

cs.SD2022★ 4 cited

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…

cs.SD2022

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…

cs.SD2022★ 1 cited

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…

eess.AS2022★ 3 cited

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…

eess.AS2022★ 5 cited

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…

cs.SD2022★ 16 cited

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…

eess.AS2022★ 1 cited

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…