Conventional standard emotion recognition systems do not apply well for the analysis of true emotional state, mostly because in standard emotion analysis the emotional state of a person is recognised over the complete utterance considering that emotions are mutually exclusive where as in real time it can also be a combination of emotions with a concealed emotion. Machine detecting these concealed information of emotion is important for emotional intelligence. It has wide applications in healthcare, aviation and defence. Statistical measures (central tendency of mean measure of spread) of autocorrelated pitch are used as features for analysing the progression of concealed emotion. The analysis of concealed emotion is performed over the synthesised concealed emotional speech signal. This speech signal is synthesised using the standard emotional speech signals (anger and neutral) from Berlin Emotional Speech (BES) database. T- statistic likelihood is used as measure for analysis of concealed emotion progressing over the synthesised speech signal. This framework is shown to provide a good tracking of emotional progressions over the synthesised concealed emotional speech signal.