# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 4 Experiments and Discussion
### Comparison with Existing Models  
We benchmarked our suggested method against several other techniques found in the existing research to ascertain its relative performance. Table 6 displays the classification accuracy scores of all these methods, highlighting how our approach stacks up against them. The selected models, including the one introduced in this research, are all effective frameworks for speech emotion recognition tasks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Pleasant** & **Disgust** & **Sad** & **Neutral** & **Fear** & **Angry** & **Happy** \\ \hline Pleasant & 0.99 & 0.00 & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 \\ \hline Disgust & 0.02 & 0.96 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline Sad & 0.00 & 0.00 & 0.97 & 0.00 & 0.01 & 0.00 & 0.00 \\ \hline Neutral & 0.00 & 0.00 & 0.00 & 0.99 & 0.00 & 0.00 & 0.00 \\ \hline Fear & 0.00 & 0.02 & 0.00 & 0.00 & 0.97 & 0.00 & 0.00 \\ \hline Angry & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 & 0.98 & 0.00 \\ \hline Happy & 0.00 & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: TESS confusion table. Figure 10: Precision, recall, and F1-score performances using the RAVDESS. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Disgust** & **Calm** & **Sad** & **Happy** & **Fear** & **Angry** & **Neutral** & **Surprise** \\ \hline Disgust & 0.98 & 0.00 & 0.01 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline Calm & 0.00 & 0.91 & 0.00 & 0.04 & 0.00 & 0.00 & 0.03 & 0.00 \\ \hline Sad & 0.00 & 0.00 & 0.93 & 0.00 & 0.05 & 0.00 & 0.00 & 0.00 \\ \hline Happy & 0.00 & 0.00 & 0.00 & 0.97 & 0.00 & 0.00 & 0.02 & 0.00 \\ \hline Fear & 0.00 & 0.00 & 0.04 & 0.00 & 0.92 & 0.02 & 0.00 & 0.00 \\ \hline Angry & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.97 & 0.00 & 0.00 \\ \hline Neutral & 0.01 & 0.00 & 0.00 & 0.8 & 0.00 & 0.00 & 0.89 & 0.00 \\ \hline Surprise & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.04 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RAVDESS confusion table. 1. [PERSON]. [44] introduced the CNN model, which was crafted with varying configurations and parameters. This model was then rigorously evaluated and benchmarked using the TESS datasets, providing a comprehensive understanding of its performance and adaptability in diverse scenarios. 2. [PERSON]. [45] presents an ANN using ReLU activation and MFCC for feature extraction, evaluated across four selected datasets. The preference for ANN over CNN arose from CNN's dependency on space-demanding image datasets and its extended training duration when compared to ANNs, which are trained on audio data. 3. The primary aim of Ref. [46] is to identify emotions from a comprehensive dataset. To do this, the authors introduced a new machine learning method. They first use mel-frequency cepstral coefficients to extract features from voice signals. Then, they employ a combined approach using the gray wolf optimizer and the naive Bayes technique for classification. 4. Ref. [47] presents a novel hybrid structure aimed at improving the precision of speech emotion recognition. This approach first extracts traditional acoustic attributes like RMS, MFCC, and zero-crossing rate. Additionally, deep features are derived from spectrogram visuals using a pre-trained ResNet101 model. These sets of features are merged to form an integrated feature vector. The ReliefH algorithm is then applied to streamline and optimize feature selection. Ultimately, the classification is executed using the support vector machine algorithm for enhanced accuracy. 5. Ref. [48] introduces a method called two-layer fuzzy multiple random forest for detecting emotions in speech. This approach combines both individual-specific and general features for emotion recognition. It employs the fuzzy C-means clustering algorithm to categorize high-dimensional emotional traits into various subgroups and then uses multiple random forests to identify the distinct emotional conditions. 6. In Ref.