maelfabien/Multimodal-Emotion-Recognition
A real time Multimodal Emotion Recognition web app for text, sound and video inputs
Real-time Facial Emotion Detection using deep learning
$ git clone https://github.com/atulapra/Emotion-detection.gitA real time Multimodal Emotion Recognition web app for text, sound and video inputs
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
Speech Emotion Classification with novel Parallel CNN-Transformer model built with PyTorch, plus thorough explanations of CNNs, Transformers, and everything in between
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
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Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation
Data from GitHub · snapshot Nov 12, 2026