scripts to model depression in speech and text
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README.md
trainLSTM.py

README.md

redbud-tree-depression

This repo contains scripts to model depression in speech and text. LSTM models are utilized to model at the segment-level of an interview (not at the word-level). The two modalities are also combined and fed into a feedforward network.

Data

The data used can be downloaded from the Distress Analysis Interview Corpus, and contains audio, video, and text of interviews with 189 subjects, about 20% of whom had some level of depression.

Features

The features are either segment-level statistics of the audio, or doc2vec embeddings of the words in a segment. Higher-level audio features (mean, max, min, median, std) were extracted using the COVAREP and FORMNAT features provided in the corpus, and the doc2vec embeddings were generated using this script. I trained using the binary outcomes as well as the multi-class outcomes.

Files

The repo contains the following files:

  • trainLSTM.py which contains the methods used to train the models.
  • requirements.txt which are the libraries used in the conda environment of this project.

Keras with the tensorflow back-end was used for modeling.

Interested in using my audio/text features? Let me know.

Libraries

I used the following librarires:

keras-gpu=2.1.3=py36_0
cudnn=7.0.5=cuda8.0_0
tensorflow-gpu=1.4.1=0
tensorflow-gpu-base=1.4.1=py36h01caf0a_0
tensorflow-tensorboard=0.4.0=py36hf484d3e_0

Reference Paper

T. Alhanai, MM. Ghassemi, J. Glass, 
"Detecting "Detecting Depression with Audio/Text Sequence Modeling of Interviews"
Interspeech 2018, India

Paper can be found here

DISCLAIMER: The user accepts the code / configuration / repo AS IS, WITH ALL FAULTS.