## 2 Related Work
### Speech Emotion Recognition  
When people engage in spontaneous conversational exchanges, their speech reveals their emotional state and personality traits in addition to the meaning of the words and their conveyance [10; 26]. Paralanguage refers to the characteristics of the voice signal that can be utilized to change the meaning of phrases and transmit emotion, either intentionally or subconsciously. [PERSON] and [PERSON] [11] offered a comprehensive overview of computational paralinguistics, addressing the primary methods, tools, and techniques for recognizing affect, emotion, and personality in human speech. [PERSON] and [PERSON] recently published an extensive review of speech emotion detection models, databases, features, preprocessing approaches, supporting modalities, and classifiers [27]. According to the reviews of [PERSON] et al. [28] and [PERSON] and [PERSON] [27], we can distinguish two main ways to perform speech emotion recognition: by using traditional classifiers or deep-learning classifiers. For many years, the tendency was to apply feature engineering from low-level descriptors and feed with them a traditional classifier, such as SVM, logistic regression, or decision tree. These descriptors contained relevant information to categorize emotions. An example framed within this line of investigation is the work of [PERSON] and [PERSON] [29]. They studied a method to obtain Mel frequency cepstral coefficients calculating the magnitude spectrum, instead of the energy spectrum, without doing a discrete cosine transform, which generated their Mel Frequency Magnitude Coefficient. However, feature engineering required large amounts of time to create and decide which descriptors were the most suitable for solving a specific task. As an attempt to reduce the researchers' efforts, some frameworks emerged to obtain these hand-crafted features automatically, such as OpenSmile [30] or Praat [31]. [PERSON] et al. 's [32] work was an example of this traditional line of investigation that employed descriptors. They passed MFCCs and spectral centroids to a bagged ensemble of support vector machines, giving an overall accuracy of 72.91% for RAVDESS. Nowadays, most publications employ deep-learning models. These classifiers are usually neural networks capable of processing these descriptors or the complete audio records. For example, [PERSON] et al. [33] suggested the use of prosody, spectral-information, and voice quality, to train a hierarchical DNN classifier, reaching an accuracy of 81.2% on RAVDESS. [PERSON] et al. [34] combined eGeMAPS features with the embeddings extracted from an xlsr-Wav2 Vec2.0 to train a CNN model. They achieved an accuracy of 77.5% by applying a global normalization on this dataset. [PERSON] et al. [35] also proposed a new method for feature extraction calculating Mel-frequency cepstral coefficients, chromagram, Mel-scale spectrogram, Tonnetz representation, and spectral contrast features from speech records. These features are the inputs of a one-dimensional CNN. Their proposal reached an accuracy of 71.61% for RAVDESS. Other works such as those proposed in [36; 37; 38] also employed CNNs, MLPs, or LSTMs to solve emotion recognition on RAVDESS using spectrograms or pre-processed features, obtaining accuracies of 80.00%, 96.18%, and 81%, respectively. Although RAVDESS is appearing in a growing number of publications, there are not an standard evaluation criteria yet, making it complex to quantify and compare contributions. For example, in [37] they achieved 96.18% accuracy using a 10-CV evaluation. Nonetheless, they did not specify how they distributed users in each fold, making it unclear whether the same user participated in the training and test sets or not. Deciding whether the distribution of users is subject-wise or not is a relevant fact to consider when implementing an evaluation setup. Non-subject-wise scenarios will always result in higher performance rates because the training and the test sets have samples of the same user.