## 5 Conclusions  
This work proposed and implemented a system for pathological voice detection using deep learning methods. The training and testing data used were recordings of the sustained vowels /a/, /i/, and /u/. Pathological records were from 259 female and 259 male subjects, and the control group was formed by samples from 259 female and 259 male subjects who were healthy and recorded at normal pitch. In order to obtain voice information from these recordings, various methods for extracting speech features were implemented, including MFCCs, LPCCs, and HOSs. In order to design the most optimal classification model, two types of classifiers based on the following deep learning methods were studied: FNN and CNN.  
The distributions of normalized skewness and kurtosis extracted from normal and pathological voice signals in men and women are described in each /a/, /i/, and /u/ vowel. The Mann-Whitney U test showed a significant difference between the normal and pathological voice signals for normalized skewness (\(p\) = 0.000) and kurtosis (\(p\) = 0.000), excluding the normalized kurtosis (\(p\) = 0.051) extracted from the female /u/ sample.  
The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter for the /u/ vowel in men. Several other combinations can be used for classification. The second-best performance, 80.77%, was obtained in the differentiation between pathological and normal voices with a combination of the FNN classifier, MFCCs, and HOSs; this was attained for the /i/ vowel samples in women. The combination of the FNN classifier, MFCCs, and MFCC deltas showed the best performance by a third candidate, 80.13%, in the /a/ vowel in men. The fourth best accuracy, 79.42%, also used the FNN and MFCC parameters in the /i/ vowel samples in women. In addition, the fifth-best  
Figure 5: The curve of the learning rate against the loss rate.  
Figure 6: Classification results from the proposed combination of CNN and LPCCs in voiced /u/ vowels in men.  
performance was 77.56% for the men's /u/ vowel samples using the CNN classifier and the MFCCs + MFCC deltas parameter. Experimenting with female or male samples with single data was more effective than experimenting with a mixture of these samples. There was also merit in combining the acoustic measures with the HOS parameters for better characterization, as both are useful when it comes to imparting important voice information. As the most important discovery of this study, the combination of various parameters and deep learning methods was useful for distinguishing normal from pathological voices.  
In future work, pathological voice detection systems could be developed to classify the stage of a specific disease and voice quality, and a monitoring function for voice disorder could also be added. Future research will need to recognize the differences between two or more diseases and voice quality. Additionally, the author will constantly study the parameters that reflect important information about pathological voice signals to realize high classification performance using various deep learning methods and artificial intelligence (AI) techniques developed in various areas [31; 32; 33; 34; 35; 36; 37]. Finally, gender analysis in the field of pathological voice signal processing needs to become more widespread.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2017R1A2B4011373). The sponsor had no involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.  
The author declares no conflict of interest.