speech-emotion-recognition-using-self-attention
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KrishnaDN
Implementation of the paper "Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning" From INTERSPEECH 2019
speech-emotion-recognition-using-self-attention
In this project we implement the paper "Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning" Published in INTERSPEECH 2019.
Please note that, some of the hyperparameters are changed in order to make the convergence better
NOTE: RESULTS ARE NOT AS GOOD AS THE PAPER. USE IT AT YOUR OWN RISK First create data_collected_full.pickle file by using the following code with respective paths
python mocap_data_collect.py
Creating 5 fold cross validation
First we need to create a pickle file for every combination of 5-fold cross validation. Use dataset.py and change the paths accordingly
python dataset.py
Training
This step trains CNN-LSTM model
python train_audio_only.py
The average accuracy is about ~53% (UW) and 52% (WA) for CNN-BLSTM. The paper reports 55%(WA) and 51% (UW) using CNN-BILSTM-ATTENTION model.
WORK IN PROGRESS Few preprocessing scripts are taken from https://github.com/Samarth-Tripathi/IEMOCAP-Emotion-Detection
Paper link : https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2594.pdf
KrishnaDN