Speech of human beings is the reflection of the state of mind. Proper evaluation of these speech signals into stress types is necessary in order to ensure that the person is in a healthy state of mind. In this work we propose a RBF classifier for speech stress classification algorithm, with sophisticated feature extraction techniques as Mel Frequency Cepstral Coefficients (MFCC). The RBF algorithm assists the system to learn the speech patterns in real time and self-train itself in order to improve the classification accuracy of the overall system. The proposed system is suitable for real time speech and is language and word independent. The human behaviour considers six basic emotions which are happiness, sadness, anger, fear, surprise & disgust. It becomes important to detect emotional state of a person which will be induced by workload, background noise, physical environmental factors (e.g. G-force) & fatigue. Broadly, stress identification becomes a scientific challenge to analyze a human being interaction with environment

RBF, MFCC, Stress Classification, Feature Selection.

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