Papers
1
Total Citations
6
H-Index
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About
Yun-Keun Lee is a researcher whose work centers on human-robot interaction (HRI), with a particular emphasis on robust speech recognition and user localization in challenging acoustic environments. His major contribution lies in developing a human-robot interface that integrates a blind source separation (BSS) algorithm, implemented through block-wise processing, to effectively filter out unknown noises and acoustic reverberations indoors. This innovation directly addresses a critical bottleneck in real-world HRI: enabling robots to reliably understand and locate users amidst ambient noise. His most-cited paper, "Human-robot interface using robust speech recognition and user localization based on noise separation device" (2009), has garnered 6 citations, serving as a foundational reference for researchers tackling noise-robust auditory interfaces. Lee’s work is notable for its practical, application-driven approach, bridging signal processing and robotics to make human-robot communication more natural and resilient. By focusing on the intersection of speech enhancement and spatial localization, he has contributed to the broader goal of creating robots that can operate effectively in unstructured, noisy environments—a key step toward intuitive and safe human-robot collaboration.
Research Focus
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Top Papers
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