6 citations · 11 across the 22 of their papers we have counts for
9 papers · 1 filter
LP-CFM: Perceptual Invariance-Aware Conditional Flow Matching for Speech Modeling
Doyeop Kwak, Youngjoon Jang, Joon Son Chung
The goal of this paper is to provide a new perspective on speech modeling by incorporating perceptual invariances such as amplitude scaling and temporal shifts. Conventional genera…
Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment
Youngjoon Jang, Liliane Momeni, Zifan Jiang +3
Our aim is to develop a unified model for sign language understanding, that performs sign language translation (SLT) and sign-subtitle alignment (SSA). Together, these two tasks en…
Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing
Zifan Jiang, Youngjoon Jang, Liliane Momeni +3
The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to continuous sign language videos. Prior…
EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training
Doyeop Kwak, Youngjoon Jang, Seongyu Kim +1
Speech signals in real-world environments are frequently affected by various distortions such as additive noise, reverberation, and bandwidth limitation, which may appear individua…
Test-Time Augmentation for Pose-invariant Face Recognition
Jaemin Jung, Youngjoon Jang, Joon Son Chung
The goal of this paper is to enhance face recognition performance by augmenting head poses during the testing phase. Existing methods often rely on training on frontalised images o…
Fork-Merge Decoding: Enhancing Multimodal Understanding in Audio-Visual Large Language Models
Chaeyoung Jung, Youngjoon Jang, Jongmin Choi +1
The goal of this work is to enhance balanced multimodal understanding in audio-visual large language models (AV-LLMs) by addressing modality bias without additional training. In cu…
AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decoding
Chaeyoung Jung, Youngjoon Jang, Joon Son Chung
Hallucination remains a major challenge in multimodal large language models (MLLMs). To address this, various contrastive decoding (CD) methods have been proposed that contrasts or…
Deep Understanding of Sign Language for Sign to Subtitle Alignment
Youngjoon Jang, Jeongsoo Choi, Junseok Ahn +1
The objective of this work is to align asynchronous subtitles in sign language videos with limited labelled data. To achieve this goal, we propose a novel framework with the follow…
Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues
Youngjoon Jang, Haran Raajesh, Liliane Momeni +2
Our objective is to translate continuous sign language into spoken language text. Inspired by the way human interpreters rely on context for accurate translation, we incorporate ad…