Shuhua Liu

Papers

Total Citations

H-Index

About

Shuhua Liu is a researcher whose work bridges artificial intelligence, robotics, and human-computer interaction, with a particular focus on speech and emotion recognition. Her most impactful contribution is the development of a novel CNN-Random Forest hybrid model for speech emotion recognition, which uses convolutional neural networks as feature extractors before feeding into a random forest classifier. This work, published in 2018, has garnered 63 citations, highlighting its significance in advancing affective computing. In the domain of multi-robot systems, Liu proposed a pursuit-evasion algorithm based on hierarchical reinforcement learning, specifically using the Option method to decompose complex tasks. This 2009 paper, with 27 citations, demonstrated superior efficiency over standard Q-learning in dynamic 2D environments. More recently, Liu has explored practical applications of human-robot interaction, including Chinese speech recognition and task analysis for the Aldebaran Nao robot. Her work addresses the critical challenge of enabling robots to understand and process Chinese speech instructions through voice preprocessing and acoustic modeling. Liu’s research is notable for its integration of deep learning with traditional machine learning methods, and its direct application to making robots more responsive and emotionally aware in real-world settings.