This project focused on supporting the testing and validation of an AI-based speech emotion recognition system designed to classify human emotions from voice inputs. The work involved organizing and tracking dataset usage, monitoring model performance, and maintaining structured documentation throughout the validation lifecycle. 

I developed tracking sheets to record testing progress, dataset distribution, and performance metrics across validation cycles. Periodic summary reports were prepared to highlight trends, identify issues, and support data-driven decision-making. The project also included issue tracking, documentation of observations, and coordination of validation workflows to ensure consistency and accuracy. 

Through this project, I gained practical exposure to AI workflow monitoring, structured reporting, dataset management, and process documentation while supporting model evaluation activities.