Interaction-aware Attention Network

• A tensorflow implementation of Interaction-aware Attention Network in An Interaction-aware Attention Network for Speech Emotion Recognition in Spoken Dialogs.

Model Overview

Data:

Data disciptions of IEMOCAP please refer to here.

Requirements

Some required libraries:

python                   >=3.6   
tensorflow-gpu           1.11.0
joblib   		 0.13.0
pandas                   0.22.0
scikit-learn             0.19.1
numpy			 1.15.3

Code:

codes	descriptions
data.py	Includes batch generator & data generator.
model.py	main codes.
hyparams.py	hyperparameters
script_train.py	testing scripts
script_test.py	training scripts

To evaluate under realistic scenarios of our model, we adopt leave-one-session-out cross validation.

Run:

For feature pooling to reduce computational cost:

python3 pool_feats.py --input_file INPUT_FILE --output_file OUTPUT_FILE --feat_dim FEAT_DIM --step STEP --max_size MAX_SIZE

For training:

python3 script_train.py --seq_dim SEQ_DIM \
                        --atten_size ATTEN_SIZE \
                        --batch_size BATCH_SIZE \
                        --model_dir MODEL_DIR \
                        --record_file outputs/RECORD_FILE.json \
                        --feat_dir data/XXX.pkl

For testing

python3 script_test.py  --result_file outputs/RECORD_FILE.json --feat_dir data/XXX.pkl

I include whole process and hyperparameters in a script:

sh run.sh

Citation

@inproceedings{yeh2019interaction,
  title={An interaction-aware attention network for speech emotion recognition in spoken dialogs},
  author={Yeh, Sung-Lin and Lin, Yun-Shao and Lee, Chi-Chun},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={6685--6689},
  year={2019},
  organization={IEEE}
}