Deep learning for Emotion Speech Recognition

For this project ,there should be a major emphasis and focus on utilizing deep learning techniques using Python and Jupyter notebook (Google colaboratory can be used as well). Data has been labeled in Github repository by any of these seven emotions -- Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear. Autoencoding.

Data Mining Methodology

It is hard to find fault in the approach. All key decisions are justified with appropriate literature.The project extends beyond applying models to complex datasets, and also competes with or outperforms other relevant works.The methodology is very thoroughly documented and completely reproducible.

Data and Python code source reference: https://github.com/declare-lab/MELD
https://github.com/MITESHPUTHRANNEU/Speech-Emotion-Analyzer

Titles references: MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations: https://arxiv.org/pdf/1810.02508.pdf

Models examples

Deep learning technique: recurrent neural network (RNN) for Automatic speech recognition

HMM (hidden Markov model), Gaussian Mixture Model (GMM)

Artificial neural network: feedforward neural network (DNN)
Deep learning arquitecture: Long short-term memory (LSTM)
Deep learning model: Convolutional neural network (CNN)