# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 1 Introduction  
Emotions are integral to human communication, profoundly influencing our interactions and behaviors. Among the various forms of expression, speech stands out as a crucial medium in which emotions are conveyed, making it a fundamental source for emotion recognition [1]. Speech emotion recognition (SER) aims to detect embedded emotions by processing and analyzing speech signals [2]. Traditional SER systems rely on acoustic features, such as intonation, rhythm, intensity, and duration, to identify patterns associated with specific emotional states [3]. These systems follow a process with a structure that includes signal acquisition, preprocessing [4], feature extraction (focusing on acoustic properties like pitch, energy, and spectral features) [1], feature selection, classification, and model evaluation [5]. The applications of SER are broad and cover different areas, within which human-computer interaction stands out where SER enables conversational agents and robots to respond empathetically, enhancing user experiences [6; 7]. In healthcare, it supports the diagnosis of mental health conditions like depression and it also aids in monitoring emotional well-being [8; 9; 10]. SER systems are also applied in call centers to improve customer service, in automotive industries for stress monitoring, and in educational technologies for personalized learning experiences [1; 11; 12]. SER models are built on two emotion representation paradigms: categorical models and dimensional models [13; 14; 15]. The categorical model identifies basic emotions universally recognized across all cultures. [PERSON] initially proposed six basic emotions--sadness, happiness, disgust, anger, fear, and surprise--emphasizing their expression through facial cues and their cross-cultural universality [16]. This concept converges with the idea of Darwinian evolution, which says that emotions are primitive responses shared among humans and animals [17]. In contrast, the dimensional model represents emotions on a continuous scale, mapping affective states onto dimensions like valence and arousal [13; 18]. Valence reflects the positive or negative evaluation of emotions, while arousal denotes its intensity or activation level [19]. Over the past two decades, SER has advanced from traditional machine learning techniques relying on handcrafted features to complex deep learning models capable of automatically extracting information-rich representations from raw data [18; 20; 21]. The most common metrics for evaluating these systems are accuracy, precision, recall, and F1-score, with the F1-score being particularly useful for imbalanced datasets [14; 15]. Techniques like leave-one-speaker-out (LOSO) provide robust benchmarking across different speakers and datasets [22]. Early approaches made use of features such as pitch, energy, and Mel-frequency cepstral coefficients (MFCCs), combined with classifiers like support vector machines (SVMs) [23; 24; 25], Hidden Markov Models (HMMs) [26; 27; 28], Gaussian Mixture Models (GMMs) [29; 30], and Naive Bayes classifiers [31; 32]. While these methods laid the groundwork for SER, they often struggled to capture the intricate emotional nuances in speech, particularly in large-scale and high-dimensional datasets [13; 33]. The emergence of deep learning transformed SER by introducing models like Convolutional Neural Networks (CNNs) [34; 35; 36], Recurrent Neural Networks (RNNs), and their variants such as Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) [37; 38; 39]. These models demonstrated superior performance by automatically learning relevant features and capturing temporal dependencies [20; 40]. Hybrid models combining CNNs with RNNs or implementing attention mechanisms, further enhanced SER capabilities by effectively modeling both spatial and temporal aspects of speech signals [40; 41; 42; 43]. Recently, the use of pre-trained models (PTMs) and self-supervised learning (SSL) has marked a significant breakthrough in SER.