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20172026
most citedHighrisk Prediction from Electronic Medical Records via Deep Attention Networks

20 citations · 27 across the 11 of their papers we have counts for

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7 papers · 1 filter

eess.AS2026

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…

eess.AS2023

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…

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…

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★ 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…

eess.AS2021

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…

eess.AS2021

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…