# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
###### Abstract  
Speech signals involve speakers' emotional states and language information, which is very important for human-computer interaction that recognizes speakers' emotions. Feature selection is a common method for improving recognition accuracy. In this paper, we propose a multi-objective optimization method based on differential evolution (MODE-NSF) that maximizes recognition accuracy and minimizes the number of selected features (NSF). First, the Mel-frequency cepstral coefficient (MFCC) features and pitch features are extracted from speech signals. Then, the proposed algorithm implements feature selection where the NSF guides the initialization, crossover, and mutation of the algorithm. We used four English speech emotion datasets, and K-nearest neighbor (KNN) and random forest (RF) classifiers to validate the performance of the proposed algorithm. The results illustrate that MODE-NSF is superior to other multi-objective algorithms in terms of the hypervolume (HV), inverted generational distance (IGD), Pareto optimal solutions, and running time. MODE-NSF achieved an accuracy of 49% using ENTERFACE05, 53% using the Ryerson audio-visual database of emotional speech and song (RAVDESS), 76% using Surrey audio-visual expressed emotion (SAVEE) database, and 98% using the Toronto emotional speech set (TESS). MODE-NSF obtained good recognition results, which provides a basis for the establishment of emotional models.  
speech signals; feature selection; multi-objective; differential evolution +
Footnote †: journal:  
Feature selection achieves dimensionality reduction by removing irrelevant and redundant features [3,4]. This is widely used in SER to reduce processing time and enhance recognition efficiency. Differential evolution (DE) mimics the natural concept of survival of the fittest, and gradually converges towards an optimal or near-optimal solution [5, 6, 7]. DE is known for its computational efficiency in optimizing feature subsets. It explores search space effectively, and it shows significant advantages in single- and multi-objective feature selection [8, 9, 10, 11].  
While multi-objective DE is fast, it has a few drawbacks. For example, it has trouble with unstable convergence and may lose population diversity. In this study, we investigated multi-objective DE to recognize speech emotion through feature selection, and the main contributions of this paper are summarized as follows:  
1. We propose a model for speech emotion recognition;
2. We propose a feature extraction approach from speech signals;
3. We propose a multi-objective feature selection algorithm based on DE in which the number of selected features (NSF) guides the initialization, crossover, and mutation of DE;
4. We validated the performance of the proposed algorithm on K-nearest neighbor (KNN) and random forest (RF) classifiers with four English speech emotion datasets.  
The structure of this paper is organized as follows. Section 2 introduces the related works of SER. Section 3 describes the proposed algorithm. Section 4 represents the experimental results with discussions, and Section 5 provides the conclusions.