Kai-Chung Cheng

Papers

Total Citations

H-Index

About

Kai-Chung Cheng is a researcher in robotics and human-robot interaction, with a primary focus on speech recognition systems for service robots. His most cited work, "HMM and BPNN based speech recognition system for home service robot" (2013, 7 citations), introduces a novel two-stage approach combining hidden Markov models (HMM) with back-propagation neural networks (BPNN). This hybrid system addresses the critical challenge of speaker-independent and robust speech recognition, enabling home service robots to accurately interpret commands from diverse users in real-world environments. By integrating statistical modeling with neural learning, Cheng’s work enhances the reliability of voice-controlled robotic assistants, a key step toward practical domestic automation. His contributions lie at the intersection of machine learning and robotics, demonstrating how adaptive algorithms can improve human-robot communication. Though his citation count is modest, the foundational nature of this research—published during the early rise of consumer robotics—underscores its relevance for subsequent work in intelligent home systems. Cheng’s approach remains a reference point for developers seeking robust, noise-tolerant speech interfaces in service robotics.