## 2 Related Work  
As a field of study, pathological voice signal processing has always aimed to create objective and accurate classifications of voice disorders. Additionally, there have been many contributions that focus on various aspects of speech processing from feature extractions to decision support systems based on deep learning methods [13; 14; 15; 16; 17; 18; 19]. This section provides a brief overview of several recent findings related to the research topic of this paper.  
[PERSON] et al. focused on feature selection (FS) and machine learning methods, such as K-nearest neighbors (KNN), random forests (RF), and support vector machines (SVM). The sustained vowels /a/, /i/, and /u/ generated by normal, high, low, and low-high-low were used. These vowels were selected from the Saarbrucken voice database, and 94 pathological subjects and 100 healthy subjects were chosen. The SVM classifier achieved the highest accuracy by reducing the feature set to 300 using the filter FS method in the original 1560 feature. The overall classification performance based on feature selection was the highest, with 80.3% for mixed samples, 80.6% for female samples, and 86.2% for male samples [13].  
A study by [PERSON] et al. focused on transfer learning strategies, such as an effective pre-trained ResNet34 model for CNN training. Due to the unequal distribution of samples, this model adjusted the weights of the samples used for the minority groups during training as a means of compensation. A three-part weight product is the weight of the final sample. A class weight (\(\alpha\)), a gender weight (\(\beta\)), as well as a gender-age weight (\(\gamma\)), each led to a final sample weight (\(\omega\)) that was calculated as \(\omega=\alpha\cdot\beta\cdot\gamma\). The 300 training samples extracted in the SVD were divided equally into 150 healthy and 150 pathological classes to ensure a balanced training process. An additional 1074 tested samples, divided into 200 healthy and 874 pathological classes, were included in the study. The system achieved a high prediction accuracy result of up to 94.54% accuracy on the training data and 95.41% accuracy on the testing data [17].  
[PERSON] et al. presented surveys of research works conducted on the automatic detection of voice disorders and explored ways to identify different types of voice disorders. They also analyzed different databases, feature extraction techniques, and machine learning approaches used in various studies. The voices were generally categorized as normal and pathological in most of the papers; however, some studies included Alzheimer's disease and Parkinson's disease (PD). Finally, this paper reviewed the performance of some of the significant research work conducted in this area [18].  
A study by [PERSON] et al. sought to evaluate the usefulness of various speech signal analysis methods in the detection of voice pathologies. First, the initial vector consisted of 28 parameters extracted from the sustained vowels /a/, /i/, and /u/ at high, low, and normal pitch in time, frequency, and cepstral domains. Subsequently, linear feature extraction techniques (principal component analysis) were used to reduce the number of parameters and select the most effective acoustic features describing speech signals. They also performed nonlinear data transformations that were calculated using kernel principal components. The initial and extracted feature vectors were classified using k-means clustering and random forest classifiers. Using random forest classification for female and male recordings, they obtained accuracies of up to 100% for the classification of healthy versus pathological voices [14].  
Other authors have also investigated the theoretical aspects of voice disorders, feature extraction techniques, and machine learning (ML) techniques, and they have also reviewed the performance of some of the significant research performed in the field of pathological voice signal processing [20; 21; 22; 23].