Shun Muramatsu

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About

Shun Muramatsu is a researcher at the forefront of human-robot interaction and affective computing, with a particular focus on how non-verbal cues shape communication. His work bridges the gap between speech information and physical behavior, exploring how subtle signals like head motion and vocal tone can reveal a speaker’s internal state. In his most-cited paper, "Estimation of Speaker’s Confidence in Conversation Using Speech Information and Head Motion" (2019, 11 citations), Muramatsu demonstrates that integrating prosodic features with kinematic data significantly improves the accuracy of confidence estimation in dialog systems. This contribution is foundational for developing more socially aware robots—machines that can sense hesitation, certainty, or doubt in real time. By moving beyond language-only models, his research addresses a critical limitation in current dialog robots, which often miss the rich, implicit information humans convey through movement and voice. Muramatsu’s work has direct implications for assistive technologies, virtual agents, and collaborative robots, where understanding a user’s confidence can lead to more natural, adaptive interactions. His findings continue to influence the design of empathetic, context-aware conversational systems.