Abstract: Human Emotion detection either through face or speech became a relatively nascent research area. Speech Emotion Acknowledgment concerns the undertaking of perceiving a speaker's feelings from their discourse chronicles. Perceiving feelings from discourse can go far in deciding an individual's physical and mental condition of prosperity. These emotions can be used for further assessment of patient’s status for better diagnosis. This paper aims to categorize emotions in speech into four different categories which are happy, sad, angry and neutral. For this analysis, four different algorithms - the Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Convolutional Neural Network (CNN-1D) are developed. Detection of Emotion through speech of an individual might be a bit hectic, because of the dynamic changes in voice signal of the same person within a very subtle period of time. So, features like mfcc, chroma, tomez contrast and mel were extracted and given to the model in order to detect the emotions. Those features were given as input to the algorithms and the empirical results implicate that Convolutional Neural Network-1D performs well comparatively. RAVDESS database is chosen for the categorization. A good recognition rate of 89% was obtained from CNN-1D.