The human behaviour which 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 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 paper we will get to know how speech Identifiers are trained and how we can enhance the basic recognition procedures by exploiting a pre-processor by use of pattern classification into different level of stress types. In this work we propose a MLP classifier for speech stress classification algorithm, with sophisticated feature extraction techniques as Mel Frequency Cepstral Coefficients (MFCC). The MLP 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.

MLP, MFCC, stress classification, feature Selection.

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