Sungmin Woo
Papers
1
Total Citations
12
H-Index
1
About
Sungmin Woo is a researcher whose work lies at the intersection of speech processing, adaptive signal enhancement, and intelligent robotics. His key contributions focus on improving speech recognition in noisy, real-world environments—a critical challenge for human-robot interaction. In his most cited work, "Combined Architecture of Adaptive Beamforming and Blind Source Separation for Speech Recognition of Intelligent Service Robots" (2007, 12 citations), Woo proposed a novel preprocessing framework that integrates adaptive beamforming with blind source separation. This approach significantly enhances the robustness of automatic speech recognition systems—such as the widely-used HTK—against high levels of background noise, a common obstacle for service robots operating in dynamic settings. By addressing the limitations of conventional front-end processing, Woo’s work has helped bridge the gap between theoretical speech models and practical deployment in noisy environments. His research underscores the importance of sensor fusion and signal conditioning in enabling more natural, reliable voice interfaces for intelligent systems. Though his citation count is modest, the targeted impact of his work on robot audition and noise-robust ASR continues to inform subsequent advances in the field.
Research Focus
Key Achievements
Top Papers
- 1