Speech Emotion Recognition (SER) represents one of the emerging fields in human-computer interaction. Quality of the human-computer interface that mimics human speech emotions relies heavily on the types of features used and also on the classifier employed for recognition. The main purpose of this paper is to present a wide range of features employed for speech emotion recognition and the acoustic characteristics of those features. Also in this paper, we analyze the performance in terms of some important parameters such as: precision, recall, F-measure and recognition rate of the features using two of the commonly used emotional speech databases namely Berlin emotional database and Danish emotional database. Emotional speech recognition is being applied in modern human-computer interfaces and the overview of 10 interesting applications is also presented in this paper to illustrate the importance of this technique.