# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 5 Conclusions  
In concluding our study, we presented an innovative speech emotion-recognition framework that synergizes ZCR, RMS, and MFCC features with advanced neural architectures, namely CNN and LSTM, enhanced by an attention mechanism. Our utilization of LSTM, as detailed by Ref. [20], tackles the challenges of long-term dependencies in recurrent networks, emphasizing the significant influence of historical emotional experiences on present states. Additionally, the attention mechanism's incorporation echoes the \"peak-end rule\" in psychology, suggesting that certain preceding states markedly influence present emotional dispositions. The CNN's role in our framework reshapes input dimensions, facilitating feature consideration through convolutional processes. With a softmax function, we ensured refined emotion classification. Our methodological advances yielded notable precision, evident from benchmarks like TESS and RAVDESS, enhancing the understanding and potential applications of SER, spanning domains from human-robot interactions to mental health diagnostics. This research not only deepens the comprehension of the SER landscape but also offers a robust foundation for future explorations in emotion recognition.  
Despite the encouraging results, our study has certain constraints. The present model predominantly centers on auditory signals, neglecting essential visual and textual indicators vital for discerning emotions. Additionally, a thorough investigation is needed to ascertain the model's effectiveness and adaptability in practical settings and with structured external datasets.  
Furthermore, examining the model's flexibility across varied linguistic and cultural backgrounds would be advantageous. Also, delving into techniques to refine the model for swifter real-time analysis [50] could pave the way for instant practical implementations.  
This manuscript was designed and written by [PERSON], with [PERSON] and [PERSON] supervising the study and contributing to the analysis and discussion of the algorithm and experimental results. All authors have read and agreed to the published version of the manuscript.  
This study is supported by Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2023, project numbers are 1415180835 (Development of International Standard Technologies based on AI Learning and Inference Technologies), 1415181638 (Establishment of standardization basis for BCI and AI Interoperability), and 1415181629 (Development of International Standard Technologies based on AI Model Lightweighting Technologies).  
Not applicable.  
This data can be found here: [[https://www.kaggle.com/datasets/eljok1/toronto-emotional-speech-set-tess](https://www.kaggle.com/datasets/eljok1/toronto-emotional-speech-set-tess)]([https://www.kaggle.com/datasets/eljok1/toronto-emotional-speech-set-tess](https://www.kaggle.com/datasets/eljok1/toronto-emotional-speech-set-tess)) and [[https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio](https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio)]([https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio](https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio)) (accessed on 1 October 2024).  
The authors [PERSON] and [PERSON] would like to express their sincere gratitude and appreciation to their supervisor, [PERSON] (Gachon University), for her support, comments, remarks, and engagement over the period in which this manuscript was written. Moreover, the authors would like to thank the editor and anonymous reviewers for the constructive comments for improving the contents and presentation of this paper.