Emotion detection is a new research era in health informatics and forensic technology .Besides having some challenges ,voice based emotion recognition is getting popular .As the situation where the facial image is not available ,the voice is the only way to detect the emotional or psychiatric condition of a person. However ,the voice signal is so dynamic even in a short-time frame so that ,a voice of the same person can differ with in a very subtle period of time. So there is a need to detect emotion of the person through his/her voice. It detects five emotion states from person’s voice (happy ,angry ,fearful ,sad ,calm). Some of the applications where we can implement this include call centers to redirect to a person when one is angry ,smart car slowing down when one is angry or fearful.
This repository contains my work on speech emotion detection using RAVDESS dataset.
The models which were discussed in the repository are SVM,Decision Tree,Random Forest.
python-3.7+
librosa
numpy
sklearn
soundfile
utils.py - Contains extraction of features,loading dataset functions
loading_data.py - Contains dataset loading,splitting data
Using_ml_algorithms.py - Contains SVM,randomforest,Decision tree Models.
CNN_speechemotion.ipynb - Consists of CNN-1d model
NOTE : Remaining .ipynb files were same as above files but shared from google colab.