Speech Emotion Recognition with High Accuracy and Large Datasets using Convolutional Neural Networks

Emotions are the best indicators of the actions of humans in advance. It is of great advantage in the current smart world. Prediction of these emotions can be able to sense the current mood of the driver and control the smart automobile accordingly and can be used in the case of chatting with customers using AI devices. This can be done by extracting the features including Mel-frequency cepstral coefficients (MFCCs) of the respective emotions and training the learning model using the classification algorithm Convolutional Neural Networks (CNN) and eventually the model can predict the emotion by comparing the newly retrieved features and the features of the training dataset and classify them accordingly.