Speech Emotion Recognition (SER) is the act of recognizing human emotion from speech. Automatic speech recognition is an active field of study in artificial intelligence and machine learning whose aim is to generate machines that communicate with people via speech.Speech is an information-rich signal that contains paralinguistic information that is conveyed by speech. A model is designed that could recognize the emotion in a speech sample. Emotion recognition is done using Support Vector Machine (SVM) as well as Multi-Layer Perceptron (MLP) Neural Network. The training for SVM was much faster when compared to MLP. In this project, we implement model using MLP classifier using voice quality features extracted from the RAVDESS -Ryerson Audio-Visual Database of Emotional Speech and Song Database. We will load the data, extract features from it, then split the dataset into training and testing sets. Then we will initialize an MLP Classifier and train the model. Finally, we’ll calculate the accuracy of our model.