Dai Hasegawa
Papers
8
Total Citations
198
H-Index
4
About
Dai Hasegawa is a leading researcher in human-agent interaction, specializing in the intersection of speech-driven gesture generation, embodied communication, and humanoid robotics. His most impactful work, the 2019 paper "Analyzing Input and Output Representations for Speech-Driven Gesture Generation," has garnered 152 citations and introduces a novel deep-learning framework that automatically generates co-speech gestures for virtual agents and robots. By extending data-driven methods with improved input-output representations, Hasegawa has significantly advanced how machines produce natural, context-aware body language from speech. He has also explored the role of embodiment in direction-giving systems, comparing robots, embodied conversational agents, and GPS interfaces to reveal how physical presence affects user comprehension. In robotics, Hasegawa has pioneered language acquisition models for humanoid robots, including methods for learning connectives through direct physical feedback and acquiring body movement verbs via physical interaction. His work on automatically selecting gestures for jokes demonstrates his commitment to making human-robot interaction more engaging and socially intelligent. With a research portfolio spanning gesture generation, language grounding, and embodied cognition, Hasegawa continues to shape how machines communicate nonverbally, bridging the gap between human expression and artificial agents.
Research Focus
Key Achievements
Top Papers
- 1Analyzing Input and Output Representations for Speech-Driven Gesture Generation152 citations · 2019
- 2The Role of Embodiment and Perspective in Direction-Giving Systems21 citations · 2010
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- 7Automatically Choosing Appropriate Gestures for Jokes2 citations · 2009
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