Ming-Tang Lee
Papers
2
Total Citations
7
H-Index
2
About
Ming-Tang Lee is a researcher specializing in robot audition, human-robot interaction, and embedded signal processing. His work focuses on enabling robots to hear and respond to human speech in noisy, real-world environments. In his most cited paper, "Wake-up-word detection for robots using spatial eigenspace consistency and resonant curve similarity" (2011, 5 citations), Lee introduced an innovative method for detecting wake-up words using microphone arrays, leveraging spatial eigenspace consistency and resonant curve similarity to improve accuracy. His earlier work, "Sound source tracking and speech enhancement by microphone array on an embedded dual-core processor platform" (2008, 2 citations), demonstrated a practical robot audition module that integrates voice activity detection, sound source localization, and speech enhancement on a dual-core ARM-DSP platform. This achievement highlights Lee’s ability to bridge algorithmic innovation with real-time embedded implementation. Though his citation counts are modest, his contributions are foundational to the development of robust, low-power auditory interfaces for autonomous robots. Lee’s research is particularly valuable for students and engineers working on speech-based human-robot interaction, offering practical solutions for wake-up word detection and sound source tracking in resource-constrained systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2