## 2 Related Work  
This section provides a concise overview of the speech emotion recognition (SER) research landscape, highlighting the importance of acoustic features [1; 2]. Acoustic parameters play a crucial role in deciphering emotions, prompting studies to investigate emotion-specific profiles through these parameters. While the integration of diverse classifiers such as Bayesian, K-nearest neighbor (KNN), and decision trees has reshaped the research field, it's worth noting that models like Gaussian mixtures (GMMs) and hidden Markov models (HMMs) may lack insights into low-level feature distribution [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. The introduction of deep learning in SER, particularly with end-to-end systems and deep neural networks (DNN), has led to significant accuracy improvements [15]. Explorations into feature fusion techniques integrating acoustic and lexical domains [16], breakthroughs like the RNN-ELM model addressing long-range context effects [17], and the preference for features like logarithmic Mel-frequency cepstral coefficients (logMel), MFCC, and extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) over prosodic features [18] underscore the evolving landscape. The convolutional recurrent neural network for end-to-end emotion prediction from speech [19] and recent studies focusing on deep learning approaches utilizing spectrograms [20; 21; 22; 23; 24; 25; 26; 27], including models with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated enhanced accuracy [21]. Ensemble models incorporating bagging and boosting techniques with support vector machines (SVMs) showcased significant accuracy gains [28]. Optimization algorithms for feature selection in SER, such as Cat Swarm Optimization (CSO), Grey Wolf Optimizer, and Enhanced Cat Swarm Optimization (ECSO), have shown promise in enhancing classification accuracy and reducing selected features [29; 30]. The Whale-Imperialist Optimization algorithm (Whale-IpCA) introduced multiple support vector neural network (Multi-SVNN) classifiers for emotion identification [31], while feature selection methods employing metaheuristic search algorithms like Cuckoo Search and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) demonstrated effective emotion classification with reduced features [32]. Comprehensive speech emotion recognition systems leverage diverse machine learning algorithms, including recurrent neural networks (RNNs), SVMs, and multivariate linear regression (MLR) [33]. Innovative feature selection approaches, such as the combination of Golden Ratio Optimization (GRO) and Equilibrium Optimization (EO) algorithms, have been explored [34; 35; 36]. Advanced architectures like dual-channel Long Short-Term Memory (LSTM) compressed capsule networks have been proposed for improved emotion recognition [37]. Furthermore, clustering-based Genetic Algorithm (GA) optimization techniques have been utilized to enhance feature sets for SER [38]. Recent research has also investigated the effectiveness of weighted binary Cuckoo Search algorithms for feature selection and emotion recognition from speech [39]. Moreover, the application of wavelet transform in SER has been explored for its potential in capturing relevant emotional features [40]. Ensemble methods like Bagged Support Vector Machines (SVMs) have shown promise in achieving robust emotion recognition from speech signals [41]. Convolutional Neural Networks (CNNs) have been employed to extract salient features for SER, leveraging their capability to capture hierarchical representations [42]. Additionally, novel methods for feature selection in SER, such as a hybrid metaheuristic approach combining Golden Ratio and Equilibrium Optimization algorithms, have been proposed [43]. Recent studies have explored the application of modulation spectral features for emotion recognition using deep neural networks [44]. Transfer learning frameworks, like EmoNet, have been developed to leverage multi-corpus data for improved SER performance [45; 46; 47].