To understand and convey each other’s intentions in a natural way, humancomputer interaction (HCI) has been paid more attentions in recent years [1]. The primary problem that the HCI faces is how to master the ability of identifying emotional information accurately, which is similar to the emotional intelligence capability in human-robot interaction [2]. As a fast and easyunderstand way for communication, speech signal was considered to identify emotions [3]. It is believed that speech conveys not only syntactic and semantic contents of the linguistic sentences but also the emotional states of humans [4]. Thus, human emotion recognition using speech signal is feasible, which mainly studies on how to identify the emotional or physical states of humans from his/her voice automatically [5]. In speech emotion recognition, one of the central research issues is how to select an optimal feature set from speech signals [6]. Most of the previous works on speech emotion recognition have been devoted on the analysis of speech prosodic features and spectral information [7]. And some novel feature parameters are used for speech emotion recognition, such as the Fourier parameters [8]. Although there are many acoustic parameters have been proven to contain emotional information, little success has been achieved in realizing such a set of features that performs consistently over different conditions [9]. Thus, most researchers prefer to use mixing feature set that is composed of many kinds of features containing more emotional information [10]. However, using mixing feature set may give rise to high dimension and redundancy of speech features, thereby it makes the learning process complicated for most machine learning algorithms and increases the likelihood of overfitting. Therefore, feature selection is indispensible to reduce the dimensions redundancy of features.