8 citations · 16 across the 15 of their papers we have counts for
16 papers
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…
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 (…
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…
Long-Form Speech Generation with Spoken Language Models
Se Jin Park, Julian Salazar, Aren Jansen +3
We consider the generative modeling of speech over multiple minutes, a requirement for long-form multimedia generation and audio-native voice assistants. However, textless spoken l…
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…
Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation
Seunghee Han, Se Jin Park, Chae Won Kim +1
Providing emotional support through dialogue systems is becoming increasingly important in today's world, as it can support both mental health and social interactions in many conve…
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…
Intuitive Multilingual Audio-Visual Speech Recognition with a Single-Trained Model
Joanna Hong, Se Jin Park, Yong Man Ro
We present a novel approach to multilingual audio-visual speech recognition tasks by introducing a single model on a multilingual dataset. Motivated by a human cognitive system whe…
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…
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…
Exploring Phonetic Context-Aware Lip-Sync For Talking Face Generation
Se Jin Park, Minsu Kim, Jeongsoo Choi +1
Talking face generation is the challenging task of synthesizing a natural and realistic face that requires accurate synchronization with a given audio. Due to co-articulation, wher…
SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory
Se Jin Park, Minsu Kim, Joanna Hong +2
The challenge of talking face generation from speech lies in aligning two different modal information, audio and video, such that the mouth region corresponds to input audio. Previ…
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning
Agus Gunawan, Muhammad Adi Nugroho, Se Jin Park
In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a bett…
Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video
Minsu Kim, Joanna Hong, Se Jin Park +1
In this paper, we introduce a novel audio-visual multi-modal bridging framework that can utilize both audio and visual information, even with uni-modal inputs. We exploit a memory…