2 citations · 2 across the 6 of their papers we have counts for
6 papers
Attention-Guided Reliability Scaling for Contrastive Decoding in Robust Audio-Visual Speech Recognition
YoungChae Kim, Da-Hee Yang, Joon-Hyuk Chang
Large language model (LLM)-based audio-visual speech recognition (AVSR) systems are robust under noise. Contrastive decoding (CD), originally introduced to stabilize LLM generation…
ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era
Masao Someki, Alexander Polok, Carlos Carvalho +14
Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort…
Latent-Level Enhancement with Flow Matching for Robust Automatic Speech Recognition
Da-Hee Yang, Joon-Hyuk Chang
Noise-robust automatic speech recognition (ASR) has been commonly addressed by applying speech enhancement (SE) at the waveform level before recognition. However, speech-level enha…
Improving Noise Robust Audio-Visual Speech Recognition via Router-Gated Cross-Modal Feature Fusion
DongHoon Lim, YoungChae Kim, Dong-Hyun Kim +2
Robust audio-visual speech recognition (AVSR) in noisy environments remains challenging, as existing systems struggle to estimate audio reliability and dynamically adjust modality…
FLOWER: Flow-Based Estimated Gaussian Guidance for General Speech Restoration
Da-Hee Yang, Jaeuk Lee, Joon-Hyuk Chang
We introduce FLOWER, a novel conditioning method designed for speech restoration that integrates Gaussian guidance into generative frameworks. By transforming clean speech into a p…
A Dual-Branch Parallel Network for Speech Enhancement and Restoration
Da-Hee Yang, Dail Kim, Joon-Hyuk Chang +2
We present a novel general speech restoration model, DBP-Net (dual-branch parallel network), designed to effectively handle complex real-world distortions including noise, reverber…