4 citations · 11 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…