Recently, classifying different emotional content of speech signals automatically has become one of the most important comprehensive inquiries. The main subject in this field is related to the improvement of the correct classification rate (CCR) resulting from the proposed techniques. However, a literature review shows that there is no notable research on finding appropriate parameters that are related to the intensity of emotions. In this article, we investigate the proper features to be employed in the recognition of emotional speech utterances according to their intensities. In this manner, 4 emotional classes of the Berlin Emotional Speech database, happiness, anger, fear, and boredom, are evaluated in high and low intensity degrees. Utilizing different classifiers, a CCR of about 70% is obtained. Moreover, a 10-fold cross-validation procedure is used to enhance the consistency of the results.