16 citations · 124 across the 40 of their papers we have counts for
7 papers · 2 filters
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…
End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection
Hemlata Tak, Jee-weon Jung, Jose Patino +3
Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments. Various approaches can be used…
Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System
Ju-ho Kim, Hye-jin Shim, Jee-weon Jung +1
Most speaker verification tasks are studied as an open-set evaluation scenario considering the real-world condition. Thus, the generalization power to unseen speakers is of paramou…
Graph Attention Networks for Anti-Spoofing
Hemlata Tak, Jee-weon Jung, Jose Patino +2
The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral sub-bands or temporal segments. Previous works show the pot…
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…