Convolutional-Neural-Network-Based-Speech-Emotion-Recognition

Speech emotion recognition (SER) has gained much attention in recent years. SER system may be efficient depending on how much useful information contained in the extracted emotional features. Many research works have achieved great results using Convolutional Neural Network with different extracted speech features. CNN classifier is used to classify eight different emotions neutral, calm, happy, sad, angry, fearful, disgust, surprised. RAVDESS dataset is used to evaluate the CNN model. Mel Frequency Cepstral Coefficients (MFCC) features are extracted from input data to capture time-frequency domain information, aimed at converting raw speech into emotional informative features from speech signals.