Automatic Depression Detection: a GRU/ BiLSTM-based Model and An Emotional Audio-Textual Corpus
https://arxiv.org/pdf/2202.08210.pdf
https://ieeexplore.ieee.org/abstract/document/9746569/
- Regression
- audio_bilstm_perm.py: train audio network
- text_bilstm_perm.py: train text network
- fuse_net.py: train multi-modal network
- Classification
- audio_features_whole.py: extract audio features
- text_features_whole.py: extract text features
- audio_gru_whole.py: train audio network
- text_bilstm_whole.py: train text network
- fuse_net_whole.py: train fuse network
The EATD-Corpus is a dataset consist of audio and text files of 162 volunteers who received counseling.
The EATD-Corpus can be downloaded at https://1drv.ms/u/s!AsGVGqImbOwYhHUHcodFC3xmKZKK?e=mCT5oN. Password: Ymj26Uv5
Training set contains data from 83 volunteers (19 depressed and 64 non-depressed).
Validation set contains data from 79 volunteers (11 depressed and 68 non-depressed).
Each folder contains depression data for one volunteer.
- {positive/negative/neutral}.wav: Raw audio in wav
- {positive/negative/neutral}_out.wav: Preprocessed audio. Preprocessing operations include denoising and de-muting
- {positive/negative/neutral}.txt: Audio translation
- label.txt: Raw SDS score
- new_label.txt: Standard SDS score (Raw SDS score multiplied by 1.25)