Emotion has an important role in naturalness of man-machine communication and many researchers investigate computerized Emotion recognition from speech in the recent decades. In this paper, the effect of formant-related features on improving the performance of Emotion detection systems is experimented. To do this, various forms and combinations of the first three formants are concatenated to a popular feature vector and Gaussian mixture models are used as classifiers. Experimental results show average recognition rate of 69% in four Emotional states and noticeable performance improvement by adding only one formant-related parameter to feature vector. The architecture of hybrid Emotion recognition/spotting is also proposed based on the developed models.