Eiichi Asakawa
Papers
1
Total Citations
11
H-Index
1
About
Eiichi Asakawa is a leading researcher in human-robot interaction and conversational gesture generation, with a focus on creating more natural, expressive virtual agents and robots. His work centers on data-driven approaches, particularly deep learning and machine learning, to automatically generate gesture motions synchronized with speech. Asakawa’s most cited paper, "Evaluation of text-to-gesture generation model using convolutional neural network" (2022, 11 citations), demonstrates his pioneering use of CNNs to map textual input to coherent, context-aware gestures, addressing a critical gap in non-verbal communication for embodied AI. This contribution has significant implications for improving user engagement and realism in social robotics, virtual assistants, and animation. By systematically evaluating model outputs, Asakawa has advanced the field’s understanding of how to assess gesture quality, moving beyond subjective judgments to more objective, data-driven metrics. His research not only enhances the technical robustness of gesture generation but also deepens our grasp of the subtle interplay between speech and body language, making him a key figure in bridging computational linguistics and robotics.
Research Focus
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Top Papers
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