## 1 Introduction  
When people get old, they experience vocal aging due to the malfunction of the respiratory system and the vocal system [1]. It is defined as presbyphonia in otorhinolaryngology [1]. Presbyphonia generally shows symptoms such as hoarse, weak, or a trembling voice due to the atrophy or loss of elasticity of the vocal cord muscles in the aging process [2]. These symptoms are similar to the major vocal symptoms of early Parkinson's disease as known as hypokinetic dysarthria that are caused by damage to the nervous system [2]. Hypokinetic dysarthria is characterized perceptually by varying degrees of reduced loudness, breathy voice, short rushes of speech, and reduced pitch variation [1, 2, 3]. However, presbyphonia can be distinguished from neurological voice disorders such as dysarthria in view of the fact that presbyphonia is not a voice disorder. Therefore, it is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders.  
Previous studies that analyzed the characteristics of presbyphonia over the past 20 years can be divided into studies that identified subjective voice problems through a self-administered questionnaire [3, 4], and acoustic phonetic studies that recorded and analyzed the voices of subjects by gender and age [5]. In particular, the acoustic phonetic studies on presbyphonia have focused on the results of tracking the cycle boundaries of the vocal cords over time using Spectrum, a frequency-based analysis in sustained vowel phonation tasks [6, 7, 8].  
More recent studies have widely used acoustical analyses such as Cepstral measures to objectively evaluate connected speech [9]. Cepstral measure is a terminology derived from Spectral measure: it is a second frequency Spectrum that is estimated by conducting the inverse Fourier transform after taking a log of the Spectrum G of the function G(t) in the time domain. Cepstral indices (e.g., Cepstral peak prominence (CPP) and Cepstral Spectral Index of Dysphonia (CSID)) can analyze aperiodic voice signals and periodic voice signals, unlike Spectrum indices (e.g., Jitter and Shimmer) [10]. In other words, it has the advantage of analyzing connected speech (dialogue) reflecting the speech characteristics of actual subject acoustically and sustained vowel phonation tasks for analyzing voice characteristics [11; 12].  
Until now, most of the acoustic studies that analyzed presbyphonia using Spectrum analysis and Cepstral analysis have evaluated the sustained phonation of single vowel [13; 14; 15], and only a few studies examined connected speech [16; 17]. Furthermore, in terms of methodology, even the previous studies [12; 16] that analyzed connected speech using Cepstral analysis just identified the differences in measurements (e.g., CPP and CSID) by age and gender based on analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), generalized linear model (GLM), and \(t\)-test by collecting the voice data of subjects.  
On the other hand, ensemble-based machine learning methods such as random forest and gradient boosting algorithm (GBM) have been widely used as data mining algorithms for developing disease prediction models in recent years [18; 19; 20]. Ensemble-based machine learning can reduce prediction errors by increasing randomness to the maximum, and it has the advantage of being able to analyze big data that is difficult to handle with traditional statistical analysis such as GLM [21]. Nevertheless, studies on hypokinetic dysarthria caused by [PERSON]'s disease have mainly relied on GLM [22], and only a few studies have used ensemble-based machine learning algorithms. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, GBM, and XGBoost and compared the prediction performance of models.