# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 1 Introduction  
Emotional states profoundly influence human interactions, with vocal expressions serving as a primary medium for displaying these emotions. Importantly, spoken language, fundamental to human interaction, goes beyond mere information exchange. It acts as a pivotal channel for expressing and transmitting a wide array of human feelings. The emotional nuances within speech are intricately woven into aspects like tone, pitch, speed, and loudness, turning it into a profound reservoir of insights. The goal of speech emotion recognition (SER) is to identify and categorize feelings by analyzing specific vocal characteristics, like mel-frequency cepstral coefficients (MFCC), root mean square energy levels (RMS), zero-crossings frequency (ZCR), tone, vocal resonances, speech length, rhythm, and sound spectrum qualities. Variations in emotion conveyed vocally are tied to shifts in temporal patterns, volume configurations, foundational pitch patterns, and resonance structures. The auditory characteristics of speech, encompassing rhythm, spectral properties, and other unique aspects, effectively highlight these distinctions. Uncovering an acoustic attribute in speech that can reliably convey emotional content is pivotal to enhancing SER. Currently, in SER studies, MFCC, ZCR, and RMS are frequently employed [1; 2; 3; 4]. This is due to their ability to effectively represent energy dynamics and the intricate time-frequency associations linked with emotional nuances. Lately, SER hasbeen incorporated into diverse sectors, including computer-user engagement, emotional comprehension assessments, psychological health studies [5], and client feedback analysis [6]. The initial challenge in SER is determining the most effective set of features for identifying emotions. Even though several features have been noted to be closely linked with emotion detection, there has not been an extensive exploration of this aspect. The subsequent challenge is discerning how to recognize emotions using the gathered features. In addressing this, our study utilizes mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), and root mean square (RMS), which are invaluable for their distinct capabilities in audio signal processing. MFCCs are particularly crucial due to their ability to represent the short-term power spectrum of sound, closely correlating with the phonetic characteristics of spoken language that vary with different emotions. ZCR provides insights into the frequency content changes of the signal, which can indicate variations in emotional intensity. RMS measures the average power and is useful for detecting the energy in a voice, often corresponding to emotional intensity. The integration of these features in our analysis has proven effective, as their combination produced the best performance, demonstrating their collective utility in enhancing emotion recognition accuracy. Over the past few years, a significant number of research efforts have shifted towards employing deep learning techniques, tailored to extract pertinent attributes from the dataset [7; 8; 9; 10; 11]. The accuracy of classification within the scope of SER challenges is largely determined by the selection and acquisition of impactful features. Numerous reputable SER research endeavors have relied on acoustic attributes such as discrete Fourier transform (DFT), ZCR, energy, MFCC, etc. [12; 13; 14]. Delving further, the proficiency of emotion classification in SER predominantly hinges on the ability to isolate and identify the most pivotal characteristics within voice recordings. These unique markers offer insights into an individual's emotional demeanor. As a result, the processes of feature extraction and determination are crucial, significantly influencing the results of the classification technique. Feature extraction plays a critical role in multi-modal SER systems, aiming to derive meaningful representations that accurately capture varying emotional states.