Objectives of Real-Time Multi-CNN-Based Personalized Emotion Recognition System for an Enhanced Virtual Team Management

Real-time Multi-Convolutional Neural Network (CNN)-based Emotion Recognition System designed specifically to enhance virtual team management. The system leverages deep learning techniques and multi-CNN architectures to accurately detect and classify emotions expressed by team members during virtual interactions. By analysing facial expressions, voice sentiment, and textual sentiment, the system provides real-time insights into team emotions, enabling proactive interventions and adaptive coaching strategies. The proposed system addresses the challenges of emotion recognition in virtual teams by integrating multi-modal data analysis, real-time processing capabilities, and personalized emotion models for team members. By capturing subtle emotional cues and contextual nuances, the system enhances communication effectiveness, fosters empathy, and improves overall team dynamics in virtual settings.