# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 2 Literature Review  
Traditionally, machine learning models in SER have relied on hand-crafted features such as signal energy, voice pitch, entropy, crossing rate, Mel-frequency cepstral coefficients (MFCC) [4; 5; 6; 7], and chroma-based features [8]. However, the effectiveness of these models often depends on the specific features selected, leading to uncertainty in their performance (e.g., inconsistent accuracy across datasets). Ongoing research has explored new features and algorithms that aim to capture the complex dynamics of feature sequences reflecting human emotions. However, the challenge remains in identifying the attributes most closely connected to different emotions to facilitate accurate predictions. Since 2015, significant advancements in deep learning techniques and increased processing capacity have led to the development of more efficient end-to-end SER systems. These systems can rapidly extract information from spectrograms or raw waveforms [9; 10],eliminating the need for manual feature extraction. Many studies suggest that employing deep learning (CNN and RNN) models based on spectrograms and raw waveforms can enhance SER performance [11; 12; 13]. [PERSON] et al. (2019) analyzed 205,000 Turkish tweets using the Tweepy Python module, finding CNNs to achieve 87% accuracy [14]. [PERSON] et al. (2021) proposed an attention-based CNN and Bi-LSTM model for sentiment analysis, combining TF-IDF and GloVe word embeddings to achieve over 90% accuracy across multiple datasets [15]. Previous work in audio emotion recognition achieved lower performance. Authors in [16] proposed hybrid MFCCT-CNN features that demonstrate superior accuracy in recent studies. A multi-task deep neural network using ResNets and a gate mechanism showed potential in SER tasks with 64.68% accuracy on the RAVDESS dataset [17]. Furthermore, Hidden Markov-based SER techniques were surveyed by [18], each addressing different challenges in emotion recognition. A hybrid model combining CNN with LSTM for sentiment analysis on RAVDESS achieved 90% accuracy [19], while BERT models [20] excelled in emotion detection in text with 92% accuracy. Innovative approaches like combining DNN with GNN for audio signal emotion recognition and multimodal sentiment analysis were also explored [21]. A lightweight neural network [22], optimized for real-time emotion recognition, achieved 80% accuracy. Several other studies [3; 23; 24; 25; 26; 27; 28; 29; 30; 31] have also made significant contributions. However, these studies often lack direct comparisons on the same dataset or detailed ablation studies, limiting their insights into model generalization. Creating sophisticated SER systems is no small task, as it demands a considerable amount of labeled training data. Without enough data, models may fail to capture the nuances of speech emotions accurately, while a lack of data can result in models becoming too specialized, limiting their broader applicability. The primary objective of this study is to train and evaluate two hybrid models using the RAVDESS dataset, focusing on achieving the highest possible accuracy while ensuring robust generalization across diverse conditions. Our major contribution lies in improving model generalization and increasing accuracy through novel architectures and noise augmentation. Additionally, we aim to conduct a comprehensive comparison of the models' performance across various training techniques and parameters to enrich insights into their efficacy and versatility, addressing a gap in prior work by validating generalization on RAVDESS and partially on SAVEE for cross-dataset insight and comparing with mainstream methods (Table 1). This research seeks to enhance both models' optimization strategies and the broader understanding of SER systems. However, this research not only advances the technical aspects of SER but also explores the ethical implications and best practices for responsible technology use.