## 1 Introduction  
Emotions play an important role in human interaction [1]. Speech is the most natural form of human expression and communication [2, 3]. Therefore, the automatic recognition of speech signals by computing devices is considered a concern [4, 5]. Words and messages are often combined to express a person's emotions [6, 7]. There are two important sources of information in a speech signal: (a) an explicit source containing linguistic content, and (b) an implicit source carrying vocal cues and non-verbal elements about speakers [8, 9]. Speech emotion recognition (SER) is an essential component of modern artificial intelligence-based systems [10, 11]. For instance, identifying the emotions of customers or drivers can lead to adaptive responses. In healthcare and education, SER has the potential to monitor patients' and students' emotional states, and aids in diagnosing conditions such as depression, anxiety, or engagement. However, it is not an easy task in real life to categorize happiness, sadness, anger, fear, disgust, surprise, and neutral emotions from speech [12, 13]. People express emotions differently across cultures and individuals, and they also convey mixed emotions. The main difficulty lies in extracting meaningful and optimal features from speech signals [14]. The characteristic of SERs is the high dimensionality of features, not all of which are correlated [15]. Many efforts have been made to improve the performance of emotional state recognition in speech through feature selection. The main aim of feature selection is to choose the most important acoustic features, which can reduce the computational cost of SERs and improve their recognition accuracy [16; 17; 18]. Researchers have applied various features to recognize emotional states, but emotion recognition is still a challenging issue. It is hard to connect speech features to specific emotions due to the lack of theoretical support, while the effectiveness of SERs is determined by the features extracted from speech signals that must be invariant to speakers and their languages. Over the years, people have utilized mel-frequency cepstral coefficients (MFCCs) to obtain acoustic features. MFCCs are essential because they capture the spectral characteristics of human speech and approximate the non-linear human auditory perception of sounds. MFCCs bridge the gap between speech's acoustic properties and its emotional content, and they provide a concise and informative representation of the spectral details in speech. Although these features carry important information about audio signals, it should be noted that the performance of recognition algorithms also subsequently decreases as the length and sampling rate of audio signals increase, requiring more calculations for analysis. DE is known for its robustness and simplicity in handling complex optimization problems [19; 20], and it is a reliable choice for SER. In this study, we investigate DE to recognize speech emotion through feature selection, and the main contributions of this paper are summarized as follows:  
1. We introduce a system for extracting acoustic features. 2. We propose an improved DE to implement feature selection. 3. The proposed mutation strategies in DE are essential for enhancing exploration and exploitation. It is possible to achieve better convergence and exploration of the speech emotion space. The jumping method introduced in DE improves global search ability and escapes local optima. 4. We validate the performance of the proposed algorithm with eNTERFACE05, the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Surrey Audio-Visual Expressed Emotion (SAEE), and the Toronto Emotional Speech Set (TESS). The algorithm provides more accurate and efficient speech emotion recognition, and it extends applications in areas such as human-computer interaction, sentiment analysis, and emotional well-being assessment. The structure of this paper is organized as follows. Section 2 introduces the related works of speech emotion recognition.