# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 1 Introduction
In the domain of acoustic feature extraction for SER, several comprehensive feature sets have been developed based on low-level descriptors (LLDs), as outlined in Refs. [15; 16; 17]. These LLDs are designed to capture essential aspects of the audio signal, which are often linked to emotional perception within specific time frames. Recently, LLDs have been transformed into utterance-level vectors using high-level statistical functions (HSFs), which aggregate temporal variations across successive frames, offering a broader perspective of the speech dynamics. While these features provide valuable insights, they are manually engineered and may not fully capture the intricate temporal dynamics of speech, as noted in Ref. [16]. This limitation underscores the need for advanced approaches that better model the temporal characteristics of emotional expressions. In response to these limitations, scholars have turned to sequential models capable of understanding temporal dynamics, including the recurrent neural network (RNN), the long-short term memory (LSTM) network, and the gated recurrent unit (GRU). Yet, these models predominantly capture forward-facing data, often overlooking the backward information that illustrates the interplay among various words. It is observed that the methods previously discussed rarely differentiate between emotional and non-emotional segments within speech, which can introduce disruptions in SER. In response to this challenge, attention mechanisms [18] are utilized to emphasize emotionally significant segments rather than considering the entire speech utterance. [PERSON]. [19] introduced an attention-based BLSTM combined with fully convolutional networks (FCN). This hybrid model was designed to autonomously discern optimal spatiotemporal representations of speech signals, particularly for in-depth spectrum feature extraction in SER endeavors. In our research, we proposed a speech emotion recognition (SER) framework that integrates ZCR, RMS, and MFCC feature sets to enhance performance. To predict emotions based on these features, we employed a combination of CNN and LSTM models with an attention mechanism. Specifically, the LSTM model introduced by [PERSON]. [20] was utilizedto address long-term dependencies in the recurrent neural network, ensuring that past learning influences future outcomes. This allows the system to account not only for the emotions currently experienced but also for those expressed in the past. Additionally, the attention mechanism was incorporated to implement the psychological principle known as the \"peak-end rule\", which suggests that certain emotional states from previous moments significantly impact the current emotional state [18]. The CNN component, on the other hand, was employed to restructure input dimensions, enabling the model to effectively extract features through convolutional operations. Finally, a softmax function was applied to classify emotions. This holistic framework integrates various components to capture both the temporal and contextual nuances of emotions, significantly enhancing the complexity and efficacy of emotion recognition in speech. The key contributions of the system put forth in this paper encompass the following aspects:  
* A new SER system has been introduced, demonstrating remarkable precision compared to that of existing foundational models. This innovative approach paves the way for promising advancements in future SER research. * We introduced a model that utilizes both CNN and LSTM architectures, integrated with an attention mechanism, to guarantee strong recognition performance, even if the number of emotion analysis categories grows. * We successfully integrated an attention mechanism with an LSTM model for speech emotion recognition, enhancing the accuracy of emotion detection by taking into account the psychological principle known as the peak-end rule. * The SER methodology presented in this study is specifically optimized for large datasets.