## 2 Literature Review
### Feature Extraction for Classification in SER
Their proposed framework employs a novel sequence selection strategy using a radial basis function network (RBFN) for measuring the similarity within clusters, selecting key segments that represent the emotional content of speech. Spectrograms generated via the STFT algorithm were used to extract discriminative features with a pre-trained Resnet101 CNN model, which were then normalized and processed using a deep BiLSTM model to capture temporal information. This approach demonstrated robustness and effectiveness, outperforming state-of-the-art methods with accuracies of up to 72.25%, 85.57%, and 77.02% on the IEMOCAP, EMO-DB, and RAVDESS datasets, respectively. [PERSON] and [PERSON] [23] proposed an attention-augmented convolutional block-gated recurrent unit network to improve SER, which they tested using the IEMOCAP dataset. This model was employed to capture the spectrogram and first- and second-order derivative aspects of audio signals, and the residual blocks allowed the CNN to extract the spatial characteristics from the inputs. The block-gated recurrent unit featured an attention layer and was used to mine the long-term data. Compared with existing methods, this network improved the weighted accuracy (WA) and unweighted accuracy (UA) by 82%, demonstrating the effectiveness of using attention-augmented convolutional-recurrent networks for SER. In another study, [PERSON] et al. [24] used five English and German benchmark datasets to present an ensemble DL framework for SER. Their ensemble used a 1D CNN-FCN (fully convolutional network) model to extract local features, a 1D CNN-LSTM-FCN model to capture long-term dependencies, and a 1D CNN-gated recurrent unit (GRU)-FCN model. Noise injection, pitch shifting, and temporal stretching were employed to increase the sample sizes. MFCC, chromogram, least-mean-square (LMS), RMS, and ZCR features were input into these models, and the ensemble model outperformed the previous techniques with significant weighted average accuracies of 99.46% for the Toronto Emotional Speech Set dataset, 95.42% for the EMO-DB dataset, 95.62% for the RAVDESS dataset, 93.22% for the SAVEE dataset, and 90.47% for the Crowd-Sourced Emotional Multimodal Actors dataset. This ensemble framework uses multiple DL architectures and DA, which enables robust SER across various languages. Thus, this research indicates that SER techniques have significant potential to improve instructor-student connections, student well-being, and personalized learning in distance education environments. For example, detecting emotional cues, such as prosody, pitch, and intensity in speech, enables the adaptation of educational materials and experiences to improve student engagement. Importantly, SER is an unobtrusive and passive tool that can be used to analyze voice data during regular online learning activities. Moreover, advances in ML and multimodal affective computing will continue to improve the accuracy of SER in remote education. Potential future directions include the detection of more complex emotions, the analysis of conversational dynamics, and the combination of speech analysis with expressions. These findings also imply that combining feature extraction and modeling procedures can increase accuracy across datasets. However, the generalizability of SER models across languages, cultures, and application sectors needs further research; despite these considerable advances, models that can properly distinguish emotions in speech across various cultures and circumstances remain in demand.