2 citations · 3 across the 6 of their papers we have counts for
6 papers
HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods
Hyun-seo Shin, Jungwoo Heo, Ju-ho Kim +3
Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems. Spoofing evidence, which helps to distinguish betw…
PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification
Wonbin Kim, Hyun-seo Shin, Ju-ho Kim +3
Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV system…
One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification
Jungwoo Heo, Chan-yeong Lim, Ju-ho Kim +2
The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in e…
SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity
Young-Joo Han, Ha-Jin Yu
Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely on large-scale noisy-clean image pairs, which are d…
Extended U-Net for Speaker Verification in Noisy Environments
Ju-ho Kim, Jungwoo Heo, Hye-jin Shim +1
Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility. Various studies have…
Graph attentive feature aggregation for text-independent speaker verification
Hye-jin Shim, Jungwoo Heo, Jae-han Park +2
The objective of this paper is to combine multiple frame-level features into a single utterance-level representation considering pairwise relationship. For this purpose, we propose…