# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 2 Related Works  
In this section, we offer a condensed review of predominant academic findings associated with SER. Through an assessment of the extant literature in this domain, we aspire to elucidate the existing knowledge landscape and identify potential gaps. This review intends to lay a robust groundwork for subsequent inquiries, propelling forward momentum in this intriguing realm of exploration. In recent history, systems anchored by deep neural network frameworks [21; 22; 23; 24; 25] have showcased remarkable achievements in identifying emotions from voice cues. Specifically, the melding of CNNs with LSTMs in integrated approaches [26; 27; 28] has proven potent in discerning both spatial attributes (via CNN) and time-bound nuances (via LSTM) in voice data. By combining these two, a comprehensive assessment of voice signals was possible, tapping into the strengths of both architectures to offer a deeper and more precise grasp of emotional nuances. Such a pioneering integration of CNN and LSTM set new standards for the evolution of speech emotion detection models, transforming the domain's trajectory. [PERSON] et al. [29] aimed to delve deeply into emotion features for identifying emotions in speech. Two distinct CNN LSTM networks were developed: one 1D for processing speech and another 2D for handling log-mel spectrograms. Each network, though specialized, follows a similar structure, incorporating four local feature learning blocks (LFLBs) and an LSTM layer. Within LFLBs, which consist of a convolutional layer and a max-pooling layer, the focus is on discerning local correlations and unveiling layered connections. The subsequent LSTM layer is tailored to understand the long-term relationships derived from the previously identified local patterns. Moreover, [PERSON] et al. [30] studied various DNN architectures for emotion detection using the IEMOCAP database [31]. The time-domain filter-learning approach outperformed other feature extraction methods. Their best model, which integrates TDNN-LSTM with time-restricted self-attention, achieved a 70.6% weighted accuracy, surpassing the previous high of 61.8%. For enhanced SER accuracy and generalization, [PERSON]. [32] used balanced augmented sampling on triple-channel log-mel spectrograms, addressing sample distribution imbalances among emotional classes and ensuring ample inputs for deep learning. They apply both time-domain and frequency-domain filters to these spectrograms, enhancing feature diversity. Subsequently, a deep neural network combining CNN and attention-based bidirectional LSTM (ABLSTM) is utilized for feature extraction, with multi-task learning integrated to optimize the network's performance. In SER, challenges arise from uncertainties in optimal feature selection and cultural emotion expression differences, with prior studies' reliance on single feature types or simple fusion potentially overlooking speech signal nuances. Thus, the Ref. [33] introduces AMSNet, a parallel network leveraging a connection attention mechanism, which merges detailed frame-level features with broader utterance-level deep features. The AMSNet incorporates two modules: a frame-level representation learning module (FRLM) emphasizing time structure with an improved attention-based LSTM, and an utterance-level representation learning module (URLM) focusing on global structure using a convolutional neural network with a squeeze-and-excitation block, both optimizing feature fusion with varied weights. Regarding feature extraction, numerous research studies have already been conducted in this area. These investigations have laid a foundation, exploring various methodologies and providing insights into best practices for deriving relevant data attributes and enhancing the understanding of the subject. In a study by [PERSON]. [34], an SVM classifier was utilized to extract speech features, including MFCC, Teager energy operator (TEO), harmonic-to-noise ratio (HNR), and ZCR.