## 2 Literature Review
### Deep Learning Models  
An ANN model for speech emotion recognition (SER) using aural datasets and Mel-Frequency Cepstral Coefficient feature extraction is presented by [PERSON] et al., showing an average accuracy of 99.52% across multiple datasets. The model categorizes audio files based on eight emotional states [30]. The goal of research on speech emotion recognition (SER) is to create human-computer interactions using emotion and speech. A study by [PERSON] et al. uses five deep learning facsimiles, including CNN-LSTM, to classify seven emotions (sadness, happiness, anger, fear, disgust, neutral, and surprise) from audio data from the Toronto Emotional Speech Set (TESS), Surrey Audio-Visual Expressed Emotion (SAVEE), and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). CNN-LSTM achieved the highest accuracy of 84.35% on the TESS+SAVEE dataset [31]. Preliminary findings from a study of Mel-Frequency Cepstral Coefficient (MFCC) features for human voice speech recognition are presented in this paper. A study by [PERSON] et al. reorganized speech from two Bangla commands using 16 Mel-scale warped cepstral coefficients. The considerable efficacy of MFCC features in speech recognition was demonstrated when a neural network trained with MFCC features was able to identify a single individual using their command [32]. The objective of the research by [PERSON] et al. was to enhance the average accuracy of speech emotion recognition (SER) models while lowering computational costs. To extract features from audio files, researchers suggest a novel method that makes use of Mel-Frequency Cepstral Coefficients (MFCCs) and 1D convolutional neural networks (1D CNNs). Compared with current models, the method achieved an average accuracy of 82.93% when tested on the Ryerson Audio-Visual Database of Emotion (RAVDESS) [33]. By using speech signals to determine gender, age group, and regional dialects, human-machine interaction systems (HMISs) can improve user interaction across sectors. To examine and differentiate feature patterns, a convolutional neural network (CNN)-based classifier and feature extraction technique were employed. The suggested model using Mel-scale frequencies (MSF) successfully recognized gender and region after mining Mel-Frequency Cepstral quantities from dialect-sentence speech signals. The classifier underwent extensive testing, validation, and training [34]. A one-dimensional dilated convolutional neural network (1D-DCNN) is proposed as a novel method for speech emotion recognition. The method enhances speech signals and extracts hidden patterns using a bi-directional gated recurrent unit (BiGRU) and hierarchical features learning blocks (HFLBs). A BiGRU receives the learned emotional features and uses them to recognize temporal cues and modify global weights. Using three benchmarked datasets, the model's accuracy was 72.75%, 91.14%, and 78.01%, respectively. A study by [PERSON] et al. suggests a speech emotion recognition model that uses segmented audio and convolutional neural networks (CNNs) to predict human emotions. Mel-Frequency Cepstral Coefficients (MFCCs) and other structures are learned by the model from audio waves. Using a combined dataset with audio segmentation and zero padding, it predicts seven emotions with 83.69% accuracy [35]. The process of classifying ambient sounds into genres, emotions, or states is called audio classification. Deep learning has gained popularity as a technique for this. The suggested system employs convolutional neural networks with 1D and 2D convolutions for increased accuracy and Mel-Frequency Cepstral Coefficient techniques for feature extraction from audio signals [36]. For applications involving human-computer interactions, speech emotion recognition is essential. Machine learning methods and hand-crafted features are frequently inadequate and computationally demanding. Robust feature extraction has been accomplished using deep learning algorithms.