End-to-end Speech Emotion Recognition using Deep Neural Networks

This repository provides training and evaluation code for our end-to-end speech emotion recognition paper. If you use this codebase in your experiments please cite:

Tzirakis, P., Zhang, J. and Schuller, B.W., 2018, April. End-to-end speech emotion recognition using deep neural networks. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5089-5093).

(https://ieeexplore.ieee.org/abstract/document/8462677)

Implementation of this method in PyTorch (along with pretrain models) can be found in our End2You toolkit

Requirements

Below are listed the required modules to run the code.

• Python <= 2.7
• NumPy >= 1.11.1
• TensorFlow <= 0.12
• MoviePy >= 0.2.2.11

Content

This repository contains the files:

• model.py: Contains the audio network.
• emotion_train.py: Performs the training of the model.
• emotion_eval.py: Performs the evaluation of the model.
• data_provider.py: Provides the data.
• data_generator.py: Creates the tfrecords from '.wav' files
• metrics.py: Contains the CCC metric that is used during evaluation.
• losses.py: Contains the CCC loss function that is used during training.