ASD Detection based on feature extractors(Auto-encoder or wav2vec2.0) and BLSTM classifier
The model training and evaluation scripts for BLSTM, AE-BLSTM-FT, AE-BLSTM-JT, W2V-BLSTM-FT, and W2V-BLSTM-JT.
For AE-BLSTM-FT, it consists of two stages. You should train the model rgrs and clsf in sequence.
python main.py [--train] [--eval] [--target_model rgrs, clsf] [--exp exp]
All the other models only uses following command:
python main.py [--train] [--eval] [--exp exp]
- Set wave files and corresponding paths in data/*.csv.
- Train model with
--train
command. You can select experiment name with--exp
argument.
python main.py --train --exp ft_test
- After the training is done, you can evaluate your model with
--eval
argument--exp
given in 2.
python main.py --eval --exp ft_test
This work was supported by the Institute of Information & communications Technology Planning & evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00330, Development of AI Technology for Early Screening of Child/Child Autism Spectrum Disorders based on Cognition of the Psychological Behavior and Response).