## 5 Conclusions
We examined the effect of speaker identity by splitting recordings from a single participant into separate folds (record-wise trials). This way, we guaranteed that theoretical _speaker-specific_ information belonged to both training and testing data. The graphs in Figure 7 illustrate our hypothesis that record-wise fold separation leads to higher accuracy than subject-wise fold separation. As such, we argue that studies employing record-wise methodologies might be reporting overoptimistic results. Further supporting this claim, record-wise RFCs using AVCA-derived coefficients, which contain two perceptual coefficient families (MFCCs and PLPs), perform better than those using DARTH-VAT-derived coefficients which contain only MFCCs. To explore the impact of mean age difference between two classes, we used one mPower subset from the baseline and replaced participants in order to shift the age distributions. As the separation between participants with PD and HC's mean age increased, classification accuracy consistently increased (Figure 8). All experiments considered, we argue that studies with large mean age differences between the two classes could be reporting overoptimistic results. We investigated the extent to which feature vector length influences classification accuracy. The smaller the number of observations compared to the feature vector length, the greater the chance that coefficients will randomly correlate with class labels. The results of these trials (Table 14) suggest that smaller corpora like Neurovoz and ItalianPVS will inherently perform better than larger ones like mPower when using large feature vector size (several times the number of observations). A way to eliminate the influence of the feature vector length in the results is to employ development folds at the expense of reducing the amount of training data. In some corpora, sustained phonation recordings have had their onset and offset removed. Our results (Figure 11) suggest that models trained with samples containing onset and offset are usually more accurate than those without. Based on this observation, we claim that employing onset and offset from sustained vowel phonations in combination with certain features (such as MFCC) could provide higher accuracy. We observed the generalization properties of our models using Neurovoz and ItalianPVS. Our results suggest that larger corpora provide better detection models. In any case, the differences in results across corpora may be caused by the different speech and voice phenotypes present in each corpus, as there might be different speech subgroups (prosodic, phonatory-prosodic, and articulatory-prosodic) associated with PD [66]. As is the nature of an academic review, this study cannot encompass every single aspect of methodological design which could possibly impact the results. For example, the following limitations exist:  
* Our analysis is limited to two classification techniques (RFC and SVM) which commonly appear in other works. We do not evaluate the performance of other classifiers found in literature, such as DNN [14; 19; 25; 26; 27; 27; 36; 58], Gaussian Mixture Model (GMM) [39; 67; 68], logistic regression [13; 22; 37], and ensemble classifiers other than RFC [12; 13; 36; 59];
* We do not have access to UPDRS/H&Y assessments of mPower and ItalianPVS participants, preventing comparison of severity variance between each corpus;
* The mPower corpus only contains sustained vowel recordings. This impacts the reliability and reproducibility of the GRBAS assessment described in Section 2.4;
* Other unexplored factors that could also impact classification accuracy include sex, recording environment, microphone specifications, recording length, type of phonation, smoking/surgical history, time between medication and recording, coexisting neurological disorders, and stage of medical treatment.