Jue Gao

Papers

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About

Jue Gao is a researcher whose work bridges artificial intelligence, human-robot interaction, and affective computing. Her most notable contribution lies in advancing speech emotion recognition (SER) for intelligent household robots, a field that aims to make machines more responsive to human emotional states. In her highly cited 2010 thesis, Gao developed a novel hybrid model combining Hidden Markov Models (HMM) with a Self-Organizing Feature Map Neural Network (SOFMNN) to classify five core emotions—joy, grief, anger, fear, and surprise—from speech signals. This work, which has garnered 51 citations, demonstrated how integrated HMM/SOFMNN algorithms could be effectively deployed on robotic platforms, enabling more natural and empathetic human-robot communication. Gao’s research is particularly significant for its practical application in domestic settings, where robots must interpret nuanced vocal cues to assist users appropriately. By focusing on emotion recognition as a key component of intelligent system design, she has contributed to foundational methods that continue to influence studies in affective computing, assistive robotics, and human-centered AI. Her work underscores the importance of emotional intelligence in technology, paving the way for more adaptive and socially aware machines.