## 4 Conclusions  
It is important to objectively distinguish between elderly and young voices because physiological aging leads to specific changes in the anatomy and physiology of all structures involved in the production and modulation of the human voice [2, 4, 6, 10, 13, 4, 7, 8, 9, 10]. Currently, most systems use all data at once, from children's voices to elderly voices, so when older people use a voice-activated system, their voices are often not recognized well [40, 41, 42]. Therefore, in order to develop a system for recognizing the voice of the elderly, it is necessary to understand the characteristics of changes in vocal cord tissue due to anatomical or physiological aging [3, 15, 16, 18].  
This study developed a classifier that distinguishes elderly voices from young voices by using deep learning algorithms and MFCCs, LPCCs, skewness, and kurtosis parameters. This study also compared the prediction performance of FNN and CNN based on the combination of various parameters to find out which combinations are best for classifying elderly and young voices. The results of this study revealed that the performance of FNN was higher than that of CNN. Therefore, future studies are necessary to compare the predictive performance estimated by diverse evaluation methods using big data of elderly and young voice signals, such as sensitivity, specificity, etc., suitable for achieving the analysis's objective, rather than using one performance index such as accuracy.  
Figure 5: Comparison of performances in terms of networks, gender, and various parameters (S: skewness; K: kurtosis; SK: skewness and kurtosis; W&M: mixed female and male data; M: male data; W: female data). Gray bars indicate FNN.  
Another key finding of this study was that skewness, one of the higher-order statistics (HOS), was the most important predictor for identifying elderly voice signals in the deep learning models (FNN and CNN). Although the fusion of the skewness and kurtosis parameters had a positive effect on the overall accuracy of the classification, the highest performance (93.75%) appeared when the skewness was added to the MFCC and MFCC delta parameters. Specifically, the study found that skewness was more effective in combination with MFCC and MFCC delta parameters, and kurtosis was more effective in combination with LPCC parameters.  
The last important discovery of this study is as follows. Most parameters estimated from male data samples demonstrated good performance. Rather than using mixed female and male data, this study recommends the development of separate systems that represents the best performance obtained for each optimized parameter by using independent data for males and females.  
In conclusion, the purpose of this study was to provide HOS parameters and deep learning methods for the classification of young and elderly voice signals; clarify the network-, gender-, and parameter-related differences regarding classification performance; and find the best combination for achieving good classification of elderly and young voice signals. The results of this study will be the basis for preparing a voice interface optimized for the elderly, such as a smart mirror system and a conversation recognition system for the elderly. Future studies will be based on the spread of elderly speech processing, along with the parameters and deep learning algorithms that reflect important information about elderly voice signals to achieve high-quality classification performance of younger and older voice signals using large datasets.  
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.