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 betwee…

Detecting Anxiety and Depression using facial emotion recognition and speech emotion recognition. Written in pythonPython

Official source code for the paper: "Reading Between the Frames Multi-Modal Non-Verbal Depression Detection in Videos"

Official source code for the paper: "It’s Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers"

A mental health quiz app to help individuals check in with themselves.

Speech-based diagnosis of depression

Official implementation of the affective mobile sensing system called FacePsy proposed in the article "FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings".

code for paper 'Spatial-Temporal Attention Network for Depression Recognition from Facial Videos'

depression detection by using tweets

A ML project specifically build for predicting students' mental health

This repository contains the code of our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022.

Depression is one of the most common mental disorders with millions of people suffering from it.It has been found to have an impact on the texts written by the affected masses.In this study our main aim was to utilise tweets to predict the possibility of a user at-risk of depression through the use of Natural Language Processing(NLP) tools and …

Detecting depressed Patient based on Speech Activity, Pauses in Speech and Using Deep learning Approach

Official Implementation for NYCU_TWD LT-EDI@ACL 2022

This project develops a Depression Detection System using Machine Learning on Twitter data. It predicts depression by analyzing tweets with SVM, Logistic Regression, Decision Trees, and NLTK in Python.

Depression web app with text emotion/depression classification and personality/depression test using 4 deep learning models. Demonstrate end-to-end pipeline from training in Python to edge deployment in Typescript

Using Machine Learning to predict if text is suicidal.

Comparing Selective Masking Methods for Depression Detection in Social Media

Edison AT is software Depression Assistant personal.

This repository applies Deep Learning techniques for depression detection in text, using LSTM, GRU, BiLSTM, BERT models, and a baseline FFNN. It also includes data visualizations, autoencoder semantics, KMeans clustering, and detailed performance comparisons.