Emotions are fundamental features of human beings, impacting their perception and everyday activities such as communication and decision making. They are expressed through speech, facial expressions, gestures and other nonverbal cues. Speech emotion recognition is the process of analyzing vocal behavior, with emphasis on nonverbal aspects of speech. Difference of emotional states can be considered as one of the important evaluation criteria to measure the performance of cognition procedures, especially for the process of decision making and action tendency. Emotion plays a significant role in influencing motivation and focus of attention. Most of the previous studies on speech emotion recognition have normally used pattern recognition methods using extracted acoustic features (such as pitch, energy and Melfrequency filter banks, Mel-frequency cepstral coefficients (MFCCs) etc.) from audio files. Most of the speech emotion categorization techniques rely on the frequency-domain stationary methods like Fourier power spectrum. These methods have been strongly questioned for non-stationary aspects of signal. Not much has been done in this area by analyzing the non-stationary aspects of the speech signal. At DGRF a rigorous non-stationary methodology capable of categorization of speech signals of various emotions is done.