24 citations · 48 across the 37 of their papers we have counts for
40 papers
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…
Robust Grounding with MLLMs Against Occlusion and Small Objects via Language-Guided Semantic Cues
Beomchan Park, Seongho Kim, Hyunjun Kim +2
While Multimodal Large Language Models (MLLMs) have enhanced grounding capabilities in general scenes, their robustness in crowded scenes remains underexplored. Crowded scenes enta…
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…
GCAgent: Long-Video Understanding via Schematic and Narrative Episodic Memory
Jeong Hun Yeo, Sangyun Chung, Sungjune Park +3
Long-video understanding remains a significant challenge for Multimodal Large Language Models (MLLMs) due to inherent token limitations and the complexity of capturing long-term te…
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…
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…
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 (…
Zero-AVSR: Zero-Shot Audio-Visual Speech Recognition with LLMs by Learning Language-Agnostic Speech Representations
Jeong Hun Yeo, Minsu Kim, Chae Won Kim +2
We explore a novel zero-shot Audio-Visual Speech Recognition (AVSR) framework, dubbed Zero-AVSR, which enables speech recognition in target languages without requiring any audio-vi…
Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
Yeonju Kim, Se Jin Park, Yong Man Ro
Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these adva…
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…
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…
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…
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…
Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech Processing
Jeong Hun Yeo, Seunghee Han, Minsu Kim +1
In visual speech processing, context modeling capability is one of the most important requirements due to the ambiguous nature of lip movements. For example, homophenes, words that…
Efficient Training for Multilingual Visual Speech Recognition: Pre-training with Discretized Visual Speech Representation
Minsu Kim, Jeong Hun Yeo, Se Jin Park +2
This paper explores sentence-level multilingual Visual Speech Recognition (VSR) that can recognize different languages with a single trained model. As the massive multilingual mode…
AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation
Jeongsoo Choi, Se Jin Park, Minsu Kim +1
This paper proposes a novel direct Audio-Visual Speech to Audio-Visual Speech Translation (AV2AV) framework, where the input and output of the system are multimodal (i.e., audio an…
DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with Diffusion
Se Jin Park, Joanna Hong, Minsu Kim +1
Speech-driven 3D facial animation has gained significant attention for its ability to create realistic and expressive facial animations in 3D space based on speech. Learning-based…
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens
Minsu Kim, Jeongsoo Choi, Soumi Maiti +3
In this paper, we propose methods to build a powerful and efficient Image-to-Speech captioning (Im2Sp) model. To this end, we start with importing the rich knowledge related to ima…
Visual Speech Recognition for Languages with Limited Labeled Data using Automatic Labels from Whisper
Jeong Hun Yeo, Minsu Kim, Shinji Watanabe +1
This paper proposes a powerful Visual Speech Recognition (VSR) method for multiple languages, especially for low-resource languages that have a limited number of labeled data. Diff…
Lip Reading for Low-resource Languages by Learning and Combining General Speech Knowledge and Language-specific Knowledge
Minsu Kim, Jeong Hun Yeo, Jeongsoo Choi +1
This paper proposes a novel lip reading framework, especially for low-resource languages, which has not been well addressed in the previous literature. Since low-resource languages…
AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model
Jeong Hun Yeo, Minsu Kim, Jeongsoo Choi +2
Visual Speech Recognition (VSR) is the task of predicting spoken words from silent lip movements. VSR is regarded as a challenging task because of the insufficient information on l…
Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation
Minsu Kim, Jeongsoo Choi, Dahun Kim +1
This paper proposes a textless training method for many-to-many multilingual speech-to-speech translation that can also benefit the transfer of pre-trained knowledge to text-based…
Text-driven Talking Face Synthesis by Reprogramming Audio-driven Models
Jeongsoo Choi, Minsu Kim, Se Jin Park +1
In this paper, we present a method for reprogramming pre-trained audio-driven talking face synthesis models to operate in a text-driven manner. Consequently, we can easily generate…
Intelligible Lip-to-Speech Synthesis with Speech Units
Jeongsoo Choi, Minsu Kim, Yong Man Ro
In this paper, we propose a novel Lip-to-Speech synthesis (L2S) framework, for synthesizing intelligible speech from a silent lip movement video. Specifically, to complement the in…