7 papers
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…
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.…