#Emotion-detection
Showing 12 of 12 repositories tagged #emotion-detection, ranked by stars
Multilingual speech understanding: ASR + emotion recognition + audio event detection. 50+ languages, 15x faster than Whisper, non-autoregressive.
Real-time Facial Emotion Detection using deep learning
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
libfaceid is a research framework for prototyping of face recognition solutions. It seamlessly integrates multiple detection, recognition and liveness models w/ speech synthesis and speech recognition.
Example projects built with the Hume AI APIs
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.
An in-depth analysis of audio classification on the RAVDESS dataset. Feature engineering, hyperparameter optimization, model evaluation, and cross-validation with a variety of ML techniques and MLP
This is a Python 3 based project to display facial expressions by performing fast & accurate face detection with OpenCV using a pre-trained deep learning face detector model shipped with the library.
This project employs emotion detection in textual data, specifically trained on Twitter data comprising tweets labeled with corresponding emotions. It seamlessly takes text inputs and provides the most fitting emotion assigned to it.
Predicting various emotion in human speech signal by detecting different speech components affected by human emotion.
Group Emotion Recognition using deep neural networks and Bayesian classifiers.
"Emotion-based-Music-Player" is a unique project designed to enhance music listening experiences. By analyzing user emotions, this player dynamically selects music tracks that match the user's mood. Through innovative algorithms, it tailors playlists to evoke desired emotional responses, offering a personalized journey through music.