# Emotion Recognition from Speech in a Subject-Independent Approach
## 4 Materials
### Human Ability to Recognize Emotions  
To assess how well the recorded emotions allow for their differentiation, experiments on emotion recognition by humans were first conducted. These involved participants listening to recordings and assigning specific emotions to them. The study involved 10 Polish-speaking individuals, raised in Central European culture, aged 18-50 years. Eachperson evaluated 80 randomly selected recordings. Two approaches to emotion recognition were tested: based on entire recordings and recordings divided into one-second segments. The results of emotion recognition for different databases are presented in Table 1. It should be stated that the primary objective of this experiment was not to conduct an extensive psychological study on a broad population but rather to gain general insight into how people manage emotion recognition based on very brief (one-second) voice segments compared to complete utterances. This test also aimed to provide an interpretive context for the results produced by the learning models--demonstrating that humans, too, exhibit low accuracy in emotion recognition from very brief voice snippets.  
The obtained results for the two databases, EMO-BAJKA and the RAVDESS, indicate differences in the effectiveness of emotion recognition depending on the length of the analyzed recording segment. For the EMO-BAJKA database, the accuracy of emotion recognition based on one-second segments is 0.40 (with a standard deviation of 0.18), whereas for entire statements, the accuracy is higher, at 0.58 (with a standard deviation of 0.14). A similar pattern is observed in the case of the RAVDESS, where the accuracy for one-second segments is 0.28 (with a standard deviation of 0.14), and for entire statements, it is 0.61 (with a standard deviation of 0.22). These results suggest that the participants were able to more accurately recognize emotions when analyzing entire statements, compared to short, one-second segments.  
In the case of the database containing Polish-language recordings, we observe an interesting phenomenon that significantly influenced the emotion recognition process. In the recorded Polish statements, keywords or phrases directly indicating emotions, such as \"I'm scared\" (\"boje sie\"), \"I'm happy\" (\"ciesze sie\"), or the expressive \"hooray\" (\"hura\"), often appeared, which could directly suggest to listeners the type of emotion presented by the speaker. Such unambiguous semantic cues, acting as clear emotional signals, undoubtedly made it easier for participants to identify emotions. It is noteworthy that such direct expressions suggesting emotions did not occur in the English-language database. The absence of such clear emotional indicators in English-language statements required the experiment participants to focus more on other aspects of the message, such as voice tone, speech pace, or intonation, to accurately determine the speaker's emotions.  
The results show that human emotion recognition from brief one-second voice clips is quite poor. Although using full utterances boosts performance, multiclass emotion classification remains challenging even for humans. This underscores the complexity of the task and supports further investigation into automated methods.