6 citations · 11 across the 11 of their papers we have counts for
12 papers
A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction
June-Woo Kim, Kangwook Jang, Minu Kim +1
Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. This study investigates the Neck…
Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses
Sungnyun Kim, Kangwook Jang, Sungwoo Cho +3
This paper introduces a new paradigm for generative error correction (GER) framework in audio-visual speech recognition (AVSR) that reasons over modality-specific evidences directl…
HuBERT-VIC: Improving Noise-Robust Automatic Speech Recognition of Speech Foundation Model via Variance-Invariance-Covariance Regularization
Hyebin Ahn, Kangwook Jang, Hoirin Kim
Noise robustness in speech foundation models (SFMs) has been a critical challenge, as most models are primarily trained on clean data and experience performance degradation when th…
ParaNoise-SV: Integrated Approach for Noise-Robust Speaker Verification with Parallel Joint Learning of Speech Enhancement and Noise Extraction
Minu Kim, Kangwook Jang, Hoirin Kim
Noise-robust speaker verification leverages joint learning of speech enhancement (SE) and speaker verification (SV) to improve robustness. However, prevailing approaches rely on im…
Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation
Sungnyun Kim, Sungwoo Cho, Sangmin Bae +2
Audio-visual speech recognition (AVSR) incorporates auditory and visual modalities to improve recognition accuracy, particularly in noisy environments where audio-only speech syste…
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition
Sungnyun Kim, Kangwook Jang, Sangmin Bae +2
Audio-visual speech recognition (AVSR) has become critical for enhancing speech recognition in noisy environments by integrating both auditory and visual modalities. However, exist…
Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis
Minu Kim, Kangwook Jang, Hoirin Kim
This paper examines how linguistic similarity affects cross-lingual phonetic representation in speech processing for low-resource languages, emphasizing effective source language s…
Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech Recognition
Sungnyun Kim, Kangwook Jang, Sangmin Bae +2
Audio-visual speech recognition (AVSR) aims to transcribe human speech using both audio and video modalities. In practical environments with noise-corrupted audio, the role of vide…
One-Class Learning with Adaptive Centroid Shift for Audio Deepfake Detection
Hyun Myung Kim, Kangwook Jang, Hoirin Kim
As speech synthesis systems continue to make remarkable advances in recent years, the importance of robust deepfake detection systems that perform well in unseen systems has grown.…
STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models
Kangwook Jang, Sungnyun Kim, Hoirin Kim
Albeit great performance of Transformer-based speech selfsupervised learning (SSL) models, their large parameter size and computational cost make them unfavorable to utilize. In th…
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation
Kangwook Jang, Sungnyun Kim, Se-Young Yun +1
Transformer-based speech self-supervised learning (SSL) models, such as HuBERT, show surprising performance in various speech processing tasks. However, huge number of parameters i…
FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learning
Yeonghyeon Lee, Kangwook Jang, Jahyun Goo +2
Large-scale speech self-supervised learning (SSL) has emerged to the main field of speech processing, however, the problem of computational cost arising from its vast size makes a…