# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 2 Related Works  
Existing research in speech emotion recognition is classified into single- and multi-objective optimization according to different goals. [PERSON] et al. proposed a SER method based on decision tree (DT), support vector machine (SVM), and Fisher feature selection [12]. The Fisher criterion is employed to filter out feature parameters with a high discrimination ability. The DT and SVM framework is first established by calculating the confusion of emotions, and then features with high discrimination are selected for each SVM in the DT according to the Fisher's criterion. Finally, SER is realized based on the model. [PERSON] et al. discussed the impact of classification methods and feature selection on the accuracy of SER, and found the best combination of methods and feature sets for stress detection in human speech [13]. Selecting appropriate parameters for a classifier is an important part of reducing computational complexity, especially for systems intended for real-time applications. The classification accuracy of an artificial neural network, KNN, and Gaussian mixture model is measured considering the selection of foreground, spectral, and speech quality features. Traditional feature selection methods often rely on supervised learning, where emotion labels are used to guide the selection of relevant features. However, these methods may not be efficient when labeled data are scarce or expensive to obtain. To address this challenge, [PERSON] et al. proposed a novel approach that leverages unsupervised feature selection algorithms to identify the most informative and discriminative features from speech data without using emotion labels [14]. They explore various unsupervised feature selection algorithms, such as principal component analysis (PCA), independent component analysis (ICA), and clustering-based methods. [PERSON] and [PERSON] integrated robust feature selection and deep transfer learning to improve the performance and robustness of speech emotion classification [15]. The robust feature selection chooses features that are less affected by noise and irrelevant variations in data. The deep transfer learning employs knowledge learned from a pre-trained neural network model on a large dataset. Transfer learning allows the model to benefit from knowledge gained from one domain (e.g., a large general speech dataset) and apply it to a related but different domain (e.g., speech emotion classification). [PERSON] et al. addressed the problem of recognizing emotions from speech signals [16]. Speech features are extracted from signals that may carry emotional cues, and these features include acoustic features (pitch, intensity, and spectrum), prosodic features (speaking rate and pitch contour), and linguistic features (lexical content and sentiment-related words). The research involvesa comprehensive analysis of the extracted speech features to identify their relevance and importance for emotion recognition. In addition to recognition accuracy, there are several studies on emotion recognition from computational efficiency, classifier optimization, and unity. [PERSON] et al. proposed a novel approach that combines heuristic feature selection methods with a multi-objective optimization framework [17], aiming to maximize classification accuracy and minimize computational complexity. They optimize computational efficiency by working in parallel and incorporating a technique for exchanging subsets of data. Furthermore, the approach uses a beneficial pre-processing step when combined with an ensemble of classifiers. It not only streamlines the feature selection process but also enhances the overall classification performance. [PERSON] and [PERSON] combined discriminative dimension reduction and a modified quantum-behaved particle swarm optimization (QPSO) algorithm to implement SER and optimize the Gaussian mixture model (GMM) classifier's parameters [18]. The dimension reduction method preserves emotion-specific features and improves the discriminative power of the extracted features.