activity
20242026
most citedAV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues

1 citations · 2 across the 10 of their papers we have counts for

collaborators

10 papers

eess.AS2026

Decoding Strategies for Diffusion-Based ASR: A Systematic Evaluation of Confidence-Based Thresholding

Jeong Hun Yeo, Minsu Kim, Hyeongseop Rha +1

While LLM-based Automatic Speech Recognition (ASR) achieves high accuracy, its speed is limited by sequential autoregressive decoding. Diffusion Language Models (DLMs) offer a para…

cs.AI2026

Diffusion Large Language Models for Visual Speech Recognition

Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha +1

Existing Visual Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on visually ambiguous tokens before suf…

cs.AI2025

Learning What to Attend First: Modality-Importance-Guided Reasoning for Reliable Multimodal Emotion Understanding

Hyeongseop Rha, Jeong Hun Yeo, Junil Won +2

In this paper, we present Modality-Importance-Guided Reasoning (MIGR), a framework designed to improve the reliability of reasoning-based multimodal emotion understanding in multim…

cs.AI2025

Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier

Hyeongseop Rha, Jeong Hun Yeo, Yeonju Kim +1

The recent advancement of Multimodal Large Language Models (MLLMs) is transforming human-computer interaction (HCI) from surface-level exchanges into more nuanced and emotionally i…

cs.CV2025

Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio

Jeong Hun Yeo, Hyeongseop Rha, Sungjune Park +2

Audio is the primary modality for human communication and has driven the success of Automatic Speech Recognition (ASR) technologies. However, such audio-centric systems inherently…

cs.CV2025

MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens

Jeong Hun Yeo, Hyeongseop Rha, Se Jin Park +1

Audio-Visual Speech Recognition (AVSR) achieves robust speech recognition in noisy environments by combining auditory and visual information. However, recent Large Language Model (…

cs.CV2024★ 1 cited

AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues

Se Jin Park, Yeonju Kim, Hyeongseop Rha +2

In human communication, both verbal and non-verbal cues play a crucial role in conveying emotions, intentions, and meaning beyond words alone. These non-linguistic information, suc…

cs.CV2024

Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language

Jeong Hun Yeo, Chae Won Kim, Hyunjun Kim +4

Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen spe…

cs.CV2024

Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation

Se Jin Park, Chae Won Kim, Hyeongseop Rha +4

In this paper, we introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response, marking…

cs.CL2024★ 1 cited

TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages

Minsu Kim, Jee-weon Jung, Hyeongseop Rha +5

The capability to jointly process multi-modal information is becoming an essential task. However, the limited number of paired multi-modal data and the large computational requirem…