# Emotion Recognition from Speech in a Subject-Independent Approach
## 7 Conclusions  
The study explored the possibility of classifying emotions based on short speech signal segments lasting one-second, using two databases: one in Polish and the other in English. The tests were conducted in a way that classifiers were trained on datasets that contained recordings from completely different individuals. This testing approach corresponds to the real-world conditions of system operation. The results of recognizing seven and eight classes of emotions indicate that this approach does not yield satisfactory results, being similar to the effectiveness of human emotion recognition. However, the use of short speech segments allows the recognition of emotions in some pairs, indicating the potential of this approach for specific tasks in human-computer interaction systems. It seems reasonable for future work to not only analyze voice features but also utilize information about the semantic content of spoken words. In the RAVDESS, emotion pairs such as Angry-Neutral and Angry-Sad achieved perfect accuracy levels of 1.000, indicating that the system recognizes these combinations of emotions with remarkable precision. Other pairs, like Angry-Happy and Disgust-Sad, also showed high accuracy, at 0.861 and 0.957, respectively, suggesting the system's solid ability to distinguish these emotions. For the EMO-BAJKA database, the highest accuracy was obtained for the Surprise-Fear pair (0.914), which may indicate the system's effectiveness in identifying these emotions. The pairs Joy-Neutral and Neutral-Fear also achieved high results, with accuracy levels of 0.914 and 0.911, respectively, showing that the system effectively differentiates these emotional states. The databases used in this study--EMO-BAJKA and the RAVDESS--contain acted emotions, which differ significantly from spontaneous emotions in terms of intensity, naturalness, and context of occurrence. This fact may limit the generalizability of the results to real-world situations. However, it is important to emphasize that datasets with acted emotions currently represent the widely accepted standard in most research on emotion recognition from speech signals--both in the classical literature and in the latest studies utilizing deep neural networks. In practice, even experienced actors encounter difficulties in expressing emotions in a manner that is both fully natural and consistent. At the same time, collecting spontaneous data with reliable emotion labels is exceptionally challenging, time-consuming, and often impossible to achieve without violating ethical guidelines. The aim of this experiment was not to realistically replicate spontaneous emotions in everyday situations, but rather to analyze the effectiveness of emotion classification under subject-independent conditions with very short time windows (1 s). In this context, the use of datasets with acted emotions is practically necessary and methodologically justified. We are aware that emotion detection in real-world conditions--with a greater number of classes, diverse contexts, and spontaneous speech--remains a significant research challenge. Verifying model performance on natural data is one of the key directions planned for future research. Additionally, future studies should explore the integration of data not only from speech, but also from gestures and facial expressions, as combining these modalities could substantially enhance the effectiveness of emotion recognition systems. Fusing such diverse data sources may yield deeper insights and a more holistic understanding of users' emotional states, paving the way for the development of advanced and more empathetic human-computer interaction systems. Furthermore, it would be valuable to investigate the application of state-of-the-art deep learning techniques, such as transformer architectures and self-supervised methods like Wave2 Vec, which have the potential to further improve emotion recognition accuracy, particularly in tasks that involve the analysis of raw speech signals. Conceptualization, A.M. and M.K.