## 1 Introduction  
The human voice represents a complex biological signal resulting from the dynamic interaction between adduction/vibration of the vocal folds and pulmonary air emission and flow through the resonant structures [1]. Physiologic aging leads to specific changes in the anatomy and physiology of all structures involved in the production and modulation of the human voice [2; 3; 4]. The aging of laryngeal tissue changes the movement of the vocal cords, their vibration, and their opening and closing processes [5]. Hence, a possible approach to evaluate the effect of physiological aging in humans could include the analysis of voice [1]. Voice characteristics are measured by the frequency of vocal cord oscillations per second, that is, the fundamental frequency (F0), jitter, shimmer, excitation source component, etc. [6; 7; 8; 9]. The elderly voice can also be distinguished from the voices of children, adolescents, middle-aged people, and seniors that have distinct acoustic features measured as different voice characteristics [5]. Therefore, in order to create 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 [10], and various welfare systems using only the voice database of the elderly should be implemented. Previous studies that analyzed the characteristics of elderly voices can be divided into studies that recognized elderly voices through machine learning methods [11; 12; 13; 14; 15; 16; 17; 18], and acoustic phonetic studies that recorded and analyzed the voices of subjects by gender and age [19; 20; 21; 22; 23; 24; 25]. In particular, deep learning studies on elderly voices have focused on the extraction of voice features calculated using computational functionals (e.g., mean, quartiles, percentiles, position of maximum and minimum, and linear regression) over acoustic low-level descriptors (LLDs), including those related to the energy, spectrum, and cepstrum of the signal [11; 12; 16; 18], and including the mel-frequency cepstral coefficients (MFCCs) [14; 15], jitter, shimmer, sound quality descriptors, and prosodic features using acoustic analysis in sustained vowel phonation tasks [10; 13; 17; 19]. More recent studies have widely used ensemble-based machine learning classifiers, such as classification and regression tree (CART), random forest, XGBoost, and the gradient boosting algorithm (GBM) to objectively distinguish presbyphonia from neurological voice disorders [10]. The key finding of another study was that cepstral peak prominence (CPP), one of the cepstral indices, was the most important predictor for identifying hypokinetic dysarthria in the developed prediction model, which included sociodemographic factors, health behavior, and acoustic-phonetic indices [26]. In other words, it had the advantage of analyzing elderly voices, reflecting the speech characteristics of actual subjects acoustically and sustained vowel phonation tasks for analyzing voice characteristics [27]. [PERSON] et al. proposed higher-order statistical (HOS) parameters for the classification of young and elderly voice signals and identifying gender- and age-related differences through HOS analysis [5]. The acoustic parameters were extracted from young and elderly voice signals using Praat and a time-frequency analysis program (TF32). Additionally, their study investigated the gender- and age-related differences in HOS parameters. Young and elderly voice signals differed significantly in normalized skewness (\(p\) = 0.005) in females and normalized kurtosis (\(p\) = 0.011) in males. Therefore, normalized skewness is a useful parameter for distinguishing between young and elderly female voices, and normalized kurtosis is essential for distinguishing between young and elderly male voices.