## 5 Conclusions  
In this study, we have analyzed the influence of potential methodological issues that may lead to overoptimistic results in the automatic classification of PwPD and HC employing sustained vowels from three different corpora. We analyzed the effects of record-wise vs subject-wise experiments, mean age difference between the two classes, cross-validation design, and feature vector length on classification accuracy. Additionally, we analyzed other factors such as vowel onset and offset removal, different feature extraction libraries, classification techniques, and performed cross-corpora trials in order to find answers to why the same techniques provide different results in different corpora. The purpose of this study _is not_ to report a precise analysis of optimizing our classifiers to achieve the highest possible accuracy. Rather, the goal of this research is to empirically demonstrate whether each aforementioned methodological factor skews classifier accuracy. In our baseline experiments, test speakers were restricted to only a single fold (subject-wise trials) and the age distributions were nearly identical. Between the three corpora, \begin{table}
\begin{tabular}{c c c c} \hline
**Rank** & **Neurovoz** & **ItalianPVS** & **mPower** \\ \hline \multicolumn{4}{c}{**DARTH-VAT Feature Ranking**} \\ \hline
1 & std\_1 st delta delta’ & PPE’ & std\_delta log energy’ \\ \hline
2 & std\_10 th delta’ & mean\_delta log energy’ & std\_MFCC\_11 th coef’ \\ \hline
3 & std\_MFCC\_10 th coef’ & mean\_MFCC\_12 th coef’ & mean\_11 th delta’ \\ \hline
4 & mean\_MFCC\_7 th coef’ & mean\_Log energy’ & mean\_MFCC\_11 th coef’ \\ \hline
5 & IMF-\textgreater{}NSR\_SEO’ & IMF-\textgreater{}NSR\_entropy’ & mean\_MFCC\_8 th coef’ \\ \hline
6 & det\_LT\_TKEO\_std\_8\_coef’ & NHR\_mean’ & mean\_MFCC\_6 th coef’ \\ \hline
7 & std\_10 th delta-delta’ & HNR\_std’ & mean\_MFCC\_5 th coef’ \\ \hline
8 & std\_6 th delta-delta’ & Shimmer-\textgreater{}F0\_DB’ & mean\_MFCC\_3 rd coef’ \\ \hline
9 & std\_4 th delta-delta’ &
\begin{tabular}{c} Shimmer-\textgreater{}F0\_PQ11\_classical\_Baken’ \\ \end{tabular} & mean\_MFCC\_2 nd coef’ \\ \hline
10 & std\_6 th delta’ &
\begin{tabular}{c} Shimmer-\textgreater{}F0\_PQ5\_classical\_Baken’ \\ \end{tabular} & mean\_MFCC\_0 th coef’ \\ \hline
11 & std\_4 th delta’ &
\begin{tabular}{c} Shimmer-\textgreater{}F0\_PQ3\_classical\_Baken’ \\ \end{tabular} & mean\_Log energy’ \\ \hline
12 & std\_1 st delta’ & mean\_7 th delta’ & IMF-\textgreater{}NSR\_SEO’ \\ \hline \multicolumn{4}{c}{AVCA Feature Ranking} \\ \hline
1 & MSHphase\_mean’ & MFCC12\_dd\_std’ & RALP75\_std’ \\ \hline
2 & MFCC7\_std’ & Hurst\_mean’ & RALA\_std’ \\ \hline
3 & MFCC4\_std’ & rFuzzyEn\_mean’ & LMR\_mean’ \\ \hline
4 & CIL\_mean’ & MarkEnt\_mean’ & MSDR12\_std’ \\ \hline
5 & PLP3\_d\_mean’ & LLE\_mean’ & MSDR10\_std’ \\ \hline
6 & PLP1\_d\_std’ & CorrDim\_std’ & MSCent11\_mean’ \\ \hline
7 & PLP5\_mean’ & rmSampEn\_mean’ & MSCent10\_std’ \\ \hline
8 & PLP3\_std’ & rGSampEn\_std’ & PLP10\_std’ \\ \hline
9 & MFCC2\_d\_mean’ & rSampEn\_std’ & PLP9\_std’ \\ \hline
10 & MFCC11\_std’ & rFftr’ & PLP7\_std’ \\ \hline
11 & MFCC2\_mean’ & rApEn\_mean’ & MFCC12\_mean’ \\ \hline
12 & PE\_std’ & PLP4\_mean’ & MFCC11\_std’ \\ \hline \end{tabular}
\end{table} 
Table 17: The 12 most important features obtained in the baseline models leading to the maximum accuracy, calculated using the ranking system described in Section 4.8.  
models trained with mPower participants perform the worst. One possible explanation may be that as shown in Figure 5, the two classes from this corpus sound more qualitatively similar on the GRBAS scale than the others. However, models trained with ItalianPVS participants predict PD more accurately than models using Neurovoz despite the similar GRBAS differences between their classes. For this reason, we claim that GRBAS ratings are inconclusive benchmarks of a classifier performance in this study.