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}
}