109 citations · 258 across the 25 of their papers we have counts for
6 papers · 1 filter
MixProLAP: Mixture-Induced Uncertainty Modeling for Probabilistic Language-Audio Pretraining
Yu Nakagome, Jaesong Lee, Soo-Whan Chung
Acoustic environments often contain multiple overlapping sound events, and the same acoustic scene can be described using diverse textual expressions, making audio-text alignment i…
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…
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…
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement
Doyeon Kim, Hyewon Han, Hyeon-Kyeong Shin +2
Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly. However, these loss terms…
FaceFilter: Audio-visual speech separation using still images
Soo-Whan Chung, Soyeon Choe, Joon Son Chung +1
The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network. Unlike previous works that…
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision
Soo-Whan Chung, Hong Goo Kang, Joon Son Chung
The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data. Recent advances in self-supervised learning have shown that effec…