Max Robotics — Global Service Robot Directory + US Certification

About

Sheng-Chieh Lee is a researcher specializing in speech processing and human-robot interaction (HRI), with a focus on enabling robust communication in noisy, real-world environments. His work centers on developing adaptive noise reduction and speech enhancement techniques to improve automatic speech recognition (ASR) for interactive robots. Lee’s most cited paper (2018, 30 citations) introduces a threshold-based noise detection and dual-procedure reduction system that significantly boosts ASR accuracy in ambient noise, a critical advancement for practical HRI applications. He further refined this approach with a noisy environment-aware speech enhancement system (2010, 5 citations) that automatically classifies and filters diverse noise types to optimize command recognition. Additionally, Lee has contributed to spatial audio processing through subspace-based direction-of-arrival (DOA) estimation with linear phase approximation and frequency bin selection (2015, 3 citations), enhancing robot perception in cluttered acoustic scenes. His cumulative work addresses the fundamental challenge of making robots reliably understand human speech outside controlled lab settings, directly impacting the deployment of service and assistive robots. Lee’s research bridges signal processing and robotics, offering practical solutions for noise-robust HRI.