Our team presented our prototype at Prototype Inspection Day and received valuable reviewer feedback. We substantially improved our web application interface, adding data exporting capabilities and an LLM-based emotion summary feature. Our audio team achieved a 80% macro-F1 score by applying weighted loss and even sampling techniques to exaggerated datasets including RAVDESS, TESS, and CREMA-D. The video team trained and tested a new ResEmoteNet model on our curated FAFE dataset. Meanwhile, our text team expanded our transcript dataset to include over 5,000 sentences evenly distributed across all seven emotion classes.

Next week, we’ll be filming and editing our promotional video to showcase our system. The audio team will evaluate our model performance on our custom dataset, while the video team continues to fine-tune new models and establish accuracy benchmarks for the FAFE dataset. Our text team will fine-tune the original roBERTa go-emotions model using our expanded transcript dataset combined with MOSEI. Based on PID feedback, we’ll enhance our webapp by incorporating Action Units with emotional data and adding separate text emotion analysis along with other suggested functionality.