## 2 Related Works  
[PERSON] et al. utilized a hybrid optimization algorithm, BBO_PSO, for emotion and stress recognition from natural speech [21]. Additionally, they employed higher-order spectral features in conjunction with the hybrid approach. These features capture the high statistical characteristics of speech signals, and they have been shown to be effective in obtaining subtle variations in speech related to emotions and stress.  
[PERSON] et al. presented a novel approach to improve the performance of SER systems for both Arabic and English languages [22]. The research focuses on developing an efficient feature selection method using the grey wolf optimizer (GWO) algorithm, which aims to identify the most relevant features from speech signals for accurate emotion recognition. To develop an agent-independent speech emotion/stress recognition system, [PERSON] et al. identified the speaker's emotion from speech where features are acquired from the OpenSmile toolbox and high-order spectral features [23]. They proposed a novel particle swarm optimization (PSO)-assisted biogeography for feature selection. [PERSON] utilized an ensemble technique that combines multiple algorithms through cat swarm optimization (CSO) [24]. This ensemble approach is designed to harness the collective intelligence of different algorithms to improve the accuracy and robustness of emotion classification.  
[PERSON] and [PERSON] also utilized pre-trained deep neural networks to extract high-level features from speech data. Transfer learning allows the model to transfer knowledge learned from a source domain (general speech data) to the target domain (emotion-specific speech data), and enhances the model's ability to generalize to new and unseen emotion samples [25]. The study emphasizes the importance of robustness in speech emotion classification, so the model performs well under different speakers, noise levels, and recording environments. Feature selection and deep transfer learning techniques are intended to improve the model's robustness.  
Depression is a prevalent mental health condition that is difficult to accurately diagnose. However, speech analysis has shown promise as a potential non-invasive and cost-effective method for depression detection. [PERSON] et al. proposed a novel approach that combines speech analysis with a quantum whale optimization algorithm (QWOA) for feature selection [26]. [PERSON] et al. recognized emotional expressions during natural phone conversations [27], and they specifically investigated individuals with recent suicidal ideation. By analyzing emotion patterns, the research examines if this vulnerable group has unique emotional expressions compared to individuals who have not recently had suicidal thoughts. [PERSON] et al. proposed a new algorithm combining DE and linear discriminant analysis (LDA) to design an efficient feature selection and classification model [28]. Auditory features are provided as input for a DE-LDA-based ESR system.  
Although the aforementioned works have produced impressive recognition results, the binary optimization characteristics of SER are not considered when using evolutionary algorithms and feature selection. The global search and local search of evolutionary algorithms cannot be well balanced. This paper proposes a new SER model that utilizes multivariate DE to balance the exploration and exploitation in feature selection, and improves recognition accuracy.