## 6 Experiment  
The experimental methodology was based on analyzing the speech signal by employing its defined generation levels. The quantification of each of the processes requires their parametric description. The experiments conducted as part of the research concentrate on 3 issues. First of all, we conducted tests with a distinction of three separate speech aspects and evaluated classifier quality based on a selected speech signal generation process. The block diagram of the conducted acoustic analysis is shown in Figure 5. Due to the fact that information is extracted based on various aspects of speech signals, additional utilization of different recordings for this purpose provides vast opportunities for studying optimization of the resultant feature vector. The second stage of the research was the application of selection descriptors. The selection of features involves reducing the original feature vector by selecting a subset of features from the original feature set. It should be stressed that this case requires establishing a criterion to serve as the selection base. This stage involved defining selections at the feature generation level for three separate subsystems. The SFFS method (sequential floating forward) was applied together with the k-nn and SVM classifier [96]. The first step within the applied method is to select one of the features. This is achieved by verifying the classification error for each of them separately. The feature that is characterized by a classification error is added to a previously created set of features. Further features are added to the previously created set in subsequent stages [97]. The feature vectors werenormalized by centering and dividing the value of their elements by standard deviations of the training data. In order to verify which of the distinguished classifiers is characterized by better properties, different hyperparameters have been determined for them. The number of neighbors in the k-nn method was limited only to \(k=1\) because preliminary research showed that increasing the number of neighbors caused an increase in testing error. The research was carried out using 4 different types of distance (Euclidean, Minkowski, Chebyshev, Spearman). The classification process using the SVM (support vector machine) algorithm was carried out with 4 different kernel functions (Linear, Cubic, Gaussian, Quadratic). The fitcsvm function available in the Matlab environment with a different type of kernel was used. The other parameters were set as default. A third aspect of this research is an attempt to integrate individual feature vectors through tight data integration [98]. This case involves a large number of features, which is a very serious problem. It increases the computational complexity of the algorithm and its memory requirements, expands the learning process, and leads to higher complexity of the classifier itself. However, most importantly, it often also causes a decrease in the number of correctly classified objects [96; 98]. It is associated with the so-called \"curse of dimensionality\". Therefore, this requires the feature selection process, which involves reducing the original feature vector by selecting a feature subset from the original feature set. It should be stressed that this case requires establishing a criterion to serve as a selection basis. We employ ranking methods for the following features: Relief, Fisher Score, \(F\)-tests, and Chi-square [97]. The diagram of experimental tests following this methodology is shown in Figure 6.  
Due to the low amount of data, a cross-validation technique was used to avoid the risk of assessments being too optimistic and unreliable. This method involves random division of the entire dataset into N folds of equal size. Next, a single fold is used to validate the model, while the remaining subsets are used in training. The process is repeated K times so that all data are used in testing and training.