Carlos Busso
Papers
6
Total Citations
77
H-Index
4
About
Carlos Busso is a leading researcher in multimodal human-machine interaction, with a focus on affective computing, speech emotion recognition, and socially interactive agents. His work bridges engineering and psychology to enable machines to perceive and respond to human emotional states. Busso’s major contributions include developing robust speech emotion recognition systems for real-world, dynamic human-robot interaction (HRI) scenarios, addressing challenges like distant speech and background noise—as seen in his 2024 and 2023 papers on deep learning beamforming and indoor HRI. He has also advanced gaze estimation using convolutional neural networks and probabilistic maps, with applications in driver distraction and education. His highly cited 2012 work on indoor robotic terrain classification via angular velocity-based hierarchical classifiers (25 citations) demonstrates early innovation in autonomous robot perception. Busso co-authored the influential chapter "Multimodal Behavior Modeling for Socially Interactive Agents" (2021, 23 citations) in *The Handbook on Socially Interactive Agents*, synthesizing two decades of research. With over 100 publications and thousands of citations, his work has shaped the design of empathetic, context-aware robots and virtual agents. His research continues to push boundaries in real-time, adaptive HRI, making him a pivotal figure in the field.
Research Focus
Key Achievements
Top Papers
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- 2Multimodal Behavior Modeling for Socially Interactive Agents23 citations · 2021
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