LI-FPN is an excellent model for depression recognition based on facial expression.

Acoustic feature extraction using Librosa library and openSMILE toolkit.使用Librosa音频处理库和openSMILE工具包，进行简单的声学特征提取

Bilkent Video Annotation Tool helps to annotate frame positions in videos.

An annotation tool for action labeling in videos. Best for machine learning/computer vision action recognition research.

A Github repository about micro-expression recognition, micro-expression detection, and micro-expression analysis

Reproduction of DepAudioNet by Ma et al. {DepAudioNet: An Efficient Deep Model for Audio based Depression Classification,(https://dl.acm.org/doi/10.1145/2988257.2988267), AVEC 2016}

Depression detection using multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models.

Classification of Sounds Using Convolutional Neural Networks

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem em…

A set of scripts that extract speech features (so far MFCCs, FBANKs, STFT, and kinda dominant frequency) and trains CNN, LSTM, or CNN+LSTM models with those features.

Deep learning using CNN for Mandarin Chinese tone classification

Depression Detection from Speech

Depression-Detection represents a machine learning algorithm to classify audio using acoustic features in human speech, thus detecting depressive episodes and patterns through sessions with user. T…

Speech emotion recognition implemented in Keras (LSTM, CNN, SVM, MLP) | 语音情感识别

Detect Depression from Social Network Using Deep learning

Detecting depression in a conversation using Convolutional Neral Network

The first asian machine learning in Jeju Island, South Korea - Project

scripts to model depression in speech and text

code repository for AVEC 2017 depression challenge

AVEC2019 challenge code

Baseline scripts for the Audio/Visual Emotion Challenge 2019

Sequence modeling benchmarks and temporal convolutional networks

Detect Depression with AI Sub-challenge (DSS) of AVEC2019 experienment version via YZK

AVEC 2013 Continuous Audio/Visual Emotion and Depression Recognition Challenge

Predicting depression from acoustic features of speech using a Convolutional Neural Network.