Jun-Wei Mao

About

Dr. Jun-Wei Mao is a leading researcher in affective computing and human-robot interaction, with a focus on enabling machines to perceive and respond to human emotions. His major contributions lie in developing robust speech and facial expression emotion recognition systems. Notably, he pioneered the use of feature selection combined with extreme learning machine decision trees for speech emotion recognition, a method that has garnered 260 citations. Dr. Mao also designed the FEER-HRI system, a four-layer framework that allows robots to both recognize human emotions and generate appropriate facial expressions, achieving 179 citations. His work on an improved brain emotion learning model for speech recognition has been cited 169 times, while his application of linear discriminant analysis with support vector machine decision trees addresses the critical challenge of high-dimensional emotional feature sets. Through these innovations, Dr. Mao has significantly advanced the field of socially intelligent robotics, bridging the gap between human emotional expression and machine understanding.

Research Focus

Key Achievements

Top Papers

1. 1
   Speech emotion recognition based on feature selection and extreme learning machine decision tree

   260 citations · 2017
2. 2
   A facial expression emotion recognition based human-robot interaction system

   179 citations · 2017
3. 3
   Speech emotion recognition based on an improved brain emotion learning model

   169 citations · 2018
4. 4
   Speech Emotion Recognition Based on Linear Discriminant Analysis and Support Vector Machine Decision Tree

   16 citations · 2018

Key Collaborators

Contact & Links