Deep-learning models of NTUA-SLP team submitted in SemEval 2018 tasks 1, 2 and 3.

Code for Stress and Affect Detection on Resource-Constrained Devices

This repository contains the code, dataset, and model outputs for the ICMI 2024 paper Multimodal User Enjoyment Detection in Human-Robot Conversation: The Power of Large Language Models. It includes scripts for prompting LLMs, training supervised models, and evaluating multimodal enjoyment detection.

Multitask Affect Recognition with CNNs — Deep Learning Assignment 1 (CS452). Implements ResNet-18 and EfficientNet-B0 for facial expression classification (8 classes) and valence/arousal regression, with training graphs, metrics (Accuracy, F1, Kappa, Alpha, AUC, RMSE, CORR, SAGR, CCC), and report.