# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 1 Introduction  
Speech signals are the most natural and practical form of human communication. They convey not only linguistic information but also a wealth of non-linguistic cues, including facial expressions and emotions. In the past decade, Speech Emotion Recognition (SER) has emerged as a promising research field, enabling computers to understand the emotional nuances in speech [1, 2]. SER examines paralinguistic voice quality cues (paralinguistics refers to non-verbal elements of communication, such as tone, pitch, volume, and facial expressions, which convey meaning beyond words), such as pitch, intonation, rhythm, and speech rate, to understand speakers' emotional states. The applications of SER extend to healthcare and education, where systems can monitor emotional well-being and provide personalized support such as tailored therapy sessions or adaptive learning environments. As technology continues to evolve, SER holds the potential to enhance human-computer interaction (HCI) and deepen our understanding of human communication. Recognizing emotions from speech signals is a complex task for several reasons. One of the main challenges is the lack of accurate and balanced speech emotion datasets, which restricts the development and evaluation of SER systems [2]. Creating high-quality speech emotion databases requires significant effort and time, as they must include a wide range of speakers across different genders, ages, languages, cultures, and emotional expressions. Additionally, emotions are often conveyed through sentences rather than individual words, making it even harder to identify emotions from speech signals. These challenges are further complicated by the need to account for cultural and linguistic differences (e.g., variations in tone or expression across languages) in emotional expression, as well as the subjective nature of emotions (i.e., how individuals perceive and express feelings differently). All these factors play a crucial role in shaping the development and performance of SER systems [1]. However, researchers are actively working to overcome these challenges, aiming to improve the accuracy and efficiency of emotion recognition from speech signals. Their efforts seek to enhance how machines understand the intricate emotional cues (e.g., subtle shifts in tone or pace) in spoken language. Beyond the words and information conveyed, the speech signal also carries the implicit emotional state (e.g., happiness or frustration) of the speaker [1]. An efficient SER system, which effectively reflects the speaker's emotions by separating acoustic components, lays the foundation for more effective HCI. SER systems are not only essential but also hold significant scientific value in health, human-machine interactions, and various other areas like behavioral analysis and customer support. The potential of these systems in enhancing communication for individuals with speech impairments or in environments where non-verbal cues are crucial further highlights their importance in interpreting and facilitating emotional expression through alternative channels such as assistive technologies or virtual assistants. The primary aims of this research are to develop and compare two deep learning models for SER, addressing the following research question: How do CNN-LSTM and Attention-Enhanced CNN-LSTM models differ in accuracy and generalization for emotion recognition? We developed these models to recognize emotions from the RAVDESS dataset [3]. This study includes a brief literature review of SER. The main aim is to compare two different deep learning models: one using a 2D CNN with LSTM and the other incorporating a bidirectional LSTM and an attention layer. We detail the methodology for creating and using these models from scratch, including techniques for improving model generalization by adding AWGN.