## 2 Literature Review
### Feature Extraction for Classification in SER  
[PERSON] and [PERSON] [16] introduced a new feature extraction technique called the Mel-frequency magnitude coefficient (MFMC) technique for SER, which they comprehensively evaluated against conventional features such as MFCCs, log frequency power coefficients (LFPCs), and linear prediction cepstral coefficients (LPCs) using six diverse emotion databases: the EMO-DB, the RAVDESS, SAVEE, EMOVO, eNTERFACE, and Urdu. They used the MFMC with an SVM classifier, which resulted in a competitive accuracy of 95.25% for the Urdu Language Speech dataset, 81.50% for the EMO-DB, 75.63% for SAVEE, 73.30% for EMOVO, 64.31% for the RAVDESS, and 56.41% for eNTERFACE. This extensive benchmarking highlights the potential of this MFMC technique in terms of SER across languages and various uses. [PERSON] et al. [17] focused on utilizing local dynamic features for SER. This resulted in the extraction of novel time-based segmentation and pitch probability distribution features from the EMO-DB, including examples of seven selected emotions. Experiments were also conducted on global features, including the MFCC, the ZCR, energy, and pitch. Their results indicated that the local dynamic pitch features outperformed the global features by achieving 70.8% accuracy, compared to the 66.4% achieved by using the global features with an SVM classifier. This suggests that the local dynamic pitch contains discriminative information that can be used to recognize emotions in speech. Similarly, [PERSON] [18] proposed an end-to-end CNN with LSTM attention mechanisms for multilingual SER, evaluating it using the multiple-language-spanning SAVEE dataset, the Arabic Natural Audio dataset (ANAD), the Basic Arabic Vocal Emotion dataset (BAVED), and the EMO-DB. The model achieved accuracies of 97.13%, 96.72%, 88.39%, and 96.72% for these databases, outperforming even custom two-dimensional CNNs. These results not only highlight the efficacy of one-dimensional (1D) CNNs in combination with LSTM models, but also attend to the learning of discriminatory representations in SER across languages. Furthermore, in response to the limited real-world emotional speech data available, [PERSON] and [PERSON] [19] analyzed DA techniques for SER by experimenting with pitch shifting and time stretching on the Japanese Twitter-Based and IEMOCAP datasets. With wav2 vec 2.0 speech embeddings and an SVM classifier, their DA approach improved the accuracy by 77.25% for these datasets. Therefore, the use of DA proved valuable in the context of a lack of naturalistic emotional speech data. [PERSON] et al. [20] used ensemble learning for multilingual cross-corpus SER by combining spectral and prosodic features using an ensemble of classifiers. They achieved an accuracy above 96.75% for the Urdu dataset, outperforming even the most state-of-the-art approaches. This demonstrates the ability of ensemble techniques to learn complementary information from different features and classifiers, thereby improving SER performance across domains. [PERSON] and [PERSON] employed an unsupervised auxiliary task in ladder networks that were used for emotional recognition [21], with emotion prediction through regression being the primary assignment. As a side task, denoising autoencoders were employed to recreate intermediate feature representations. Thus, the framework was trained in a semi-supervised manner using a large amount of unlabeled data from the domain of interest. This approach proved more effective than fully supervised single-task learning and multi-task learning (STL and MTL) baselines because within-corpus evaluations determined an increase from 3.0% to 3.5% in the concordance correlation coefficient (CCC) as a result of this architecture, while cross-corpus evaluations indicated that the CCC increased from 16.1% to 74.1%. [PERSON] et al. [22] introduced a clustering-based framework for SER that leverages learned features and a deep BiLSTM network to enhance the recognition accuracy while reducing the computational complexity.