# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 5 Conclusions  
We present advanced techniques for SER by creating two hybrid models. Both models perform well despite the challenges in SER systems due to input variability. The bidirectional LSTM model offers slightly higher accuracy due to its bidirectional learning and attention mechanism, although it provides minimal additional advantages. The second model, while more complex and requiring extensive training, makes slightly better predictions. This precision can be attributed to a well-balanced dataset, strategic data augmentation to reduce losses, and careful management of convolution blocks and layers. Furthermore, the models exhibit high generalizability, which proves effective even in noisy data conditions. Selecting the right hyperparameters and using the Mel Spectrogram as input allow the models to achieve excellent results. SER is vital for AI and research and focuses on recognizing emotions from speech signals by creating advanced models. This work utilized stacked CNN and LSTM layers to analyze Mel spectrograms. The first model used these networks effectively, while the second model incorporated a bidirectional LSTM with an attention layer to enhance feature detection. Both models demonstrated strong classification accuracy, with the first model reaching 96.5% and the second model achieving 98.1%. Model 2's attention mechanism improved its predictive performance but made it more complex and slower to train compared to Model 1. This research demonstrates the significant potential of the proposed hybrid models for SER applications, though certain limitations remain. The current evaluation was conducted primarily on the RAVDESS dataset, which features English-language speech with North American accents. To further test generalizability, we also evaluated the model on the SAVEE [34] dataset, another widely used English speech emotion dataset. The model correctly classified 5 out of 7 randomly selected emotion samples (tested using our pre-trained model without retraining on SAVEE), indicating promising generalization across similar English-accented datasets. However, generalizability to non-English or multilingual contexts remains unverified. Due to linguistic and cultural variations in emotional expression, additional testing on diverse datasets is needed for broader applicability. Moreover, this study did not incorporate multimodal analysis (e.g., combining audio and video); this may limit real-world applicability in complex scenarios. Despite these constraints, both models demonstrated strong capabilities in controlled environments. This comparison underscores the importance of thoughtful architectural design. Future work will focus on refining these models, experimenting with advanced feature extraction techniques, and expanding evaluations to include larger multilingual datasets and noisy, multimodal environments in the real world, including customer service and mental health monitoring systems. [PERSON] gathered the materials and conducted the experiments. All of the authors conceived the experiments. Specifically, [PERSON] and [PERSON] dealt with designing the methodology and worked on the literature review, while [PERSON] and [PERSON] analyzed the results and thoroughly reviewed the manuscript. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is funded by PNRR project FAIR--Future AI Research (PE00000013), Spoke 9--Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU.