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.

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

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

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.

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'

Speech-based diagnosis of depression

depression detection by using tweets

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.

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.

A ML project specifically build for predicting students' mental health

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

Official Implementation for NYCU_TWD LT-EDI@ACL 2022

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 deep learning algorithms.LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid Bi-LSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988 respectively,giving an F1 score of 0.9971.

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.

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

Edison AT is software Depression Assistant personal.

Machine Learning Project for Depression Detection Using Tweets.

Comparing Selective Masking Methods for Depression Detection in Social Media

This consists in using a variety of social networks data, including both images and texts, to detect early signs of depression.

Depression and anxiety detection on social media with ML - first place in UTD's 2024 AI workshop

Official codebase for "Context Aware Deep Learning for Multi Modal Depression Detection" [ICASSP 2019, Oral]

Using Machine Learning to predict if text is suicidal.

OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition

Towards Explainable Multimodal Depression Recognition for Clinical Interviews

This is an implementation of the attention-based hybrid architecture (Ghosh et al, 2023) for suicide/depressive social media notes detection.

A mobile application to detect the depression level in patients by facial and Twitter analysis.

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Extract explainablity from RoBERTa 🪆 ad Born 🐈 while classifying depresson 🎭