10 citations · 24 across the 34 of their papers we have counts for
13 papers · 1 filter
Multimodal Temporal Modeling for Continuous Group Emotion Recognition in Multi-party Dialogues
Soma Iwata, Koji Inoue, Muyun Wu +3
To realize natural behavior in dialogue agents in multi-party dialogue scenarios, it is important to understand group emotion such as valence and arousal as a whole. Most prior wor…
TEIDAN: A Multilingual Multiparty Dialogue Corpus
Taiga Mori, Koji Inoue, Mikey Elmers +2
Multi-party interaction is a central setting for human communication and a necessary target for human-agent interaction systems that must participate in group conversation. Yet ava…
Human-robot conversation with multiple participants in noisy public spaces
Divesh Lala, Yogeeswaran Muthukumaran, Vincent Fernandes +10
For noisy real-world environments such as those in open public spaces, spoken dialogue systems for both autonomous robots and avatars should be carefully designed to provide enhanc…
CultureConverse: A Multilingual Multi-turn Simulation Harness for Culturally Grounded Assistance in East and Southeast Asia
Bryan Chen Zhengyu Tan, Weihua Zheng, Thong T. Doan +30
Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common use case: users seeking pract…
MemUse: Moving Memory Evaluation from Direct QA to Natural Integration in Long-Term Human-AI Conversation
Ryuichi Sumida, Koji Inoue, Tatsuya Kawahara
Memory systems for conversational LLMs are conventionally evaluated by direct, fact-seeking questions about prior dialogue (Direct QA): can the model recall fact X from a prior con…
Does Listening Matter? Backchanneling and Nodding in AI Clone
Koji Inoue, Kazushi Kato, Tatsuya Kawahara +1
AI clones that imitate a specific person typically reproduce what the person says and how they sound, but not how they listen. We investigate whether adding multimodal listening be…
Real-time Generation of Listener Nodding via Prediction of Kinematic Parameters for Avatar Dialogue Systems
Kazushi Kato, Koji Inoue, Taiga Mori +2
In human dialogue, we achieve smooth communication by expressing nonverbal cues such as eye contact, nodding, and facial expressions with precise timing. It is expected for convers…
Low-Latency Turn-Taking via Context-Aware Preface Generation in a Real-World Dialogue Robot
Yuki Okafuji, Koji Inoue, Yoshiki Ohira
Large language model (LLM)-based dialogue systems suffer response delays because generation begins only after final speech recognition. While fixed fillers are a workaround, they b…
On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels
Taiga Mori, Koji Inoue, Divesh Lala +1
In multi-party dialogues between a dialogue system and multiple users, identifying to whom an utterance is addressed is a key challenge. Prior work has typically treated addressee…
Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Multimodal Study of Human-AI Interaction
Ryuichi Sumida, Mao Saeki, Masaki Eguchi +4
As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship. We present a longitudinal multimodal study of…
I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue System
Zi Haur Pang, Yahui Fu, Koji Inoue +1
Emotional validation - explicitly acknowledging that a user's feelings make sense - has proven therapeutic value but has received little computational attention. Emotional validati…
Toward Signing Activity Projection in Sign Language Interaction
Takao Obi, Wang Yusong, Koji Inoue +1
Social robots must interact robustly not only with users assumed by speech-centered systems but also with diverse users whose communication relies on different modalities, e.g., si…
Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language
Yoshiki Tanaka, Ryuichi Uehara, Koji Inoue +1
Emotion Recognition in Conversation (ERC) is critical for enabling natural human-machine interactions. However, existing methods predominantly employ categorical or dimensional emo…