The purpose of this project is to create a language independent application that can correctly identify emotions in a speaker’s voice. The emotions considered in this project are: Happy, Sad, Angry and Fearful. This voice emotion recognition project uses the RAVDESS professional voice actor dataset to use as the input speech signal. The acoustic characteristic of the speech signal is feature. Feature extraction is the process that extracts a small amount of data from the speech signal. Many feature extraction methods are available and Mel Frequency Cepstral Coefficient (MFCC) is the commonly used method. This project MFCC cepstral coefficient along with frequency domain FFT of speech signal are extracted. The features are used to create a classifier using SVM with linear kernel. As a result of completing the above procedure, it is evident that MFCC with FFT works best to classify happiness and sadness in speech. The classifier performs reasonably well with the emotion of anger but sparsely with the emotion of fear, thereby, giving an accuracy of 75%. These findings may be useful in understanding the use of an SVM classifier by extracting MFCC and FFT from speech in the field of emotion recognition. They can be implement to create smart devices that can process human emotions and respond accordingly; innovating the way humans and machines interact with each other.