StraysWonderland / deep-emotion-sense

CNN for language-independent emotion prediction on a dataset of English and French speech samples.

deep-emotion-sense

Convolutional neural network to predict language-independent emotion on a dataset of English and French speech.

This model is trained on a merged dataset of natural speech with data provided by IEMOCAP for english speech samples and Recola for french speech samples.

The implementation is a basis for a research paper on comparison on different optimization algorithms and activation functions.

Getting Started

You can view the notebook here.

Run the notebook

Prerequisites

• Python 3
• Tensorflow

Starting the notebook

Simply open a new terminal in the directory and type:

> jupyter notebook

Built With

• Tensorflow

Contributors

• F. Strohm - StrohmFn
• C. Tasci - StraysWonderland

Experimental Results

Englisch testset

Class	Mono-lingual	Multi-lingual	Cross-language
Sadness	0.000	0.015	0.015
Anger	0.019	0.014	0.014
Pleasure	0.043	0.010	0.120
Joy	0.942	0.985	0.864
MICRO	0.421	0.432	0.405

French testset

Class	Mono-lingual	Multi-lingual	Cross-language
Sadness	0.070	0.000	0.230
Anger	0.200	0.200	0.200
Pleasure	0.350	0.035	0.357
Joy	0.754	0.912	0.403
MICRO	0.533	0.524	0.359