FromNature's Stars
covarep/covarep
A Cooperative Voice Analysis Repository for Speech Technologies
feelins/Praat_Scripts
Some basic praat scripts.
Xiaobin-Rong/gtcrn
The official implementation of GTCRN, an ultra-lite speech enhancement model.
Kurama622/PicoPebble
A lightweight distributed machine learning training framework for beginners
muzixingyun/LI-FPN
LI-FPN is an excellent model for depression recognition based on facial expression.
zlzhang1124/AcousticFeatureExtraction
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
selimfirat/bilkent-video-annotation-tool
Bilkent Video Annotation Tool helps to annotate frame positions in videos.
devyhia/action-annotation
An annotation tool for action labeling in videos. Best for machine learning/computer vision action recognition research.
Vision-Intelligence-and-Robots-Group/awesome-micro-expression-recognition
A Github repository about micro-expression recognition, micro-expression detection, and micro-expression analysis
adbailey1/DepAudioNet_reproduction
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}
badcannon/Depression-Detection-Audio
Alpha-Raj/Depression-Detection
Depression detection using multi-modal fusion framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models.
nnarenraju/sound-classification
Classification of Sounds Using Convolutional Neural Networks
gionanide/Speech_Signal_Processing_and_Classification
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 employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
a-n-rose/Build-CNN-or-LSTM-or-CNNLSTM-with-speech-features
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.
alicex2020/Mandarin-Tone-Classification
Deep learning using CNN for Mandarin Chinese tone classification
mprerana/DepressionReg
chanjunweimy/FYP_Submission
Depression Detection from Speech
belal981/depression-detection
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. The method is tailored to lower the entry barrier when finding help mental disorder and diagram-support for medical professionals ours.
Renovamen/Speech-Emotion-Recognition
Speech emotion recognition implemented in Keras (LSTM, CNN, SVM, MLP) | 语音情感识别
isrugeek/depression_detection
Detect Depression from Social Network Using Deep learning
sukesh167/Depression-Detection-in-speech
Detecting depression in a conversation using Convolutional Neral Network
niquejoe/Classification-of-Depression-on-Social-Media-Using-Text-Mining
The first asian machine learning in Jeju Island, South Korea - Project
talhanai/redbud-tree-depression
scripts to model depression in speech and text
jyfeather/UWdepressionX
code repository for AVEC 2017 depression challenge
paulomann/avec-dds-2019
AVEC2019 challenge code
AudioVisualEmotionChallenge/AVEC2019
Baseline scripts for the Audio/Visual Emotion Challenge 2019
locuslab/TCN
Sequence modeling benchmarks and temporal convolutional networks
LouisYZK/dds-avec2019
Detect Depression with AI Sub-challenge (DSS) of AVEC2019 experienment version via YZK
wangzh3/USER-EMOTION-ANALYSIS-AND-DEPRESSION-RECOGNITION-SYSTEM-BASED-ON-HUMAN-COMPUTER-INTERACTION