Chinonso Paschal Udeh

Ministry of Education of the People's Republic of China

Papers

Total Citations

H-Index

About

Chinonso Paschal Udeh is a rising researcher at the intersection of artificial intelligence, affective computing, and human-robot interaction (HRI). His work centers on developing advanced deep learning architectures for emotion recognition, with a particular focus on improving the accuracy and efficiency of models that interpret human emotional states from speech and facial expressions. Udeh’s most cited paper, “Improved ShuffleNet V2 network with attention for speech emotion recognition” (2024, 16 citations), introduces a lightweight yet powerful neural network that integrates attention mechanisms to better capture emotional cues in audio. His earlier work, “Multimodal Facial Emotion Recognition Using Improved Convolution Neural Networks Model” (2023, 7 citations), addresses the critical challenge of enabling robots to perceive and respond to human emotions—a cornerstone for natural HRI. By enhancing model performance while maintaining computational efficiency, Udeh’s contributions are paving the way for more empathetic and socially aware robotic systems. His research holds promise for applications in mental health monitoring, assistive robotics, and interactive AI, marking him as a notable emerging voice in the field of emotion-aware machine learning.