# Emotion Recognition from Speech in a Subject-Independent Approach
## 6 Results and Discussion
### The Impact of Testing Strategies on the Accuracy of Emotion Recognition  
The obtained results of emotion recognition and classification strongly depend on how data (examples) are divided into training, validation, and testing sets. Decisions regarding data division are crucial for evaluating the effectiveness of emotion recognition models and can lead to varied results depending on the adopted methodology. Popular approaches to this division include subject-independent approaches (where examples from the same individuals do not appear in both training and testing data), random splitting, and the use of cross-validation (CV). In Table 3, an overview of the testing strategies used by us is presented, along with their abbreviated names and a description of how the data are divided into training, validation, and testing sets. In the SS-70-10-20 approach, no recordings of the same individuals are used in the training, validation, and test sets. In the RS-70-10-20 case, the dataset is divided into training, validation, and testing sets by randomly selecting examples. This division ensures that each sample has an equal chance of being in any of the sets, increasing representativeness and reducing the risk of bias. Thisapproach is preferred when the order of examples is not significant, and the model should be capable of generalizing based on diverse data. In five-fold cross-validation, the dataset is divided into five equal parts, and the training and testing process is repeated five times. Each time, a different part of the dataset serves as the test set, while the remainder serves as the training set. Similarly, in 10-fold cross-validation, this process is repeated 10 times with the dataset divided into 10 parts. Cross-validation allows better utilization of data since each sample is used for both training and testing. This is particularly useful when data are limited. Both 5-CV and 10-CV provide a more reliable evaluation of model performance than a single split into datasets, as they minimize the impact of random division on results. Individual examples are typically chosen randomly for training, validation, and testing sets, so each sample has an equal chance of being in any of the sets: training, validation, and testing. This approach was also applied by the authors. Then we conducted research aimed at understanding how dividing data into training and testing sets affects the effectiveness of emotion classification for two datasets: EMO-BAJKA and the RAVDESS. For emotion classification, we used all calculated 96 features. In the studies, a support vector machine with a cubic kernel was used as the classifier, operating on one-second speech segments containing emotions. Windows were shifted every 0.1 s. The results obtained using the SVM classifier for different data splitting methods into training and testing sets were compared. The goal was to understand to what extent the choice of data splitting method can impact the final effectiveness of emotion recognition systems. Table 4 presents the classification accuracies. Three classification trials were conducted for the RS-70-10-20, 5-CV, and 10-CV strategies and one for the SS-70-10-20 strategy. At this stage of the research, data from the validation set were not used to optimize the model. Analyzing the obtained results, it is noticeable that the cross-validation methods (5-CV and 10-CV) and the random splitting of data, RS-70-10-20, lead to significantly better classification results compared to SS-70-10-20. In the case of RS-70-10-20, effectiveness significantly increased, reaching values around 0.925-0.947 for both databases. Further improvements are seen in the 5-CV and 10-CV methods, where effectiveness increases even more, reaching levels of 0.945-0.960 for the RAVDESS and 0.949-0.966 for EMO-BAJKA. Cross-validation, involving multiple random divisions of data into training and testing sets and averaging the results, provides a more robust and reliable estimation of the model's generalization capability.