## 4 Results & Discussion
### Influence of Speaker Identity on Classification Accuracy  
Table 13 includes the results from trials where we used record-wise fold creation rather than subject-wise, which was used in the baseline. Figure 7 includes the comparison between the maximum accuracy obtained when using subject-wise versus record-wise folds for the two feature extraction libraries employed in this study. As described in Section 3.4.2, recordings from the same speaker belong to both the training and testing data sets. Compared to the baseline results shown in Table 12 (in which the same speakers' recordings belong to only one of the training/testing folds), the accuracy obtained in this new scenario is higher in most cases. Given the potential impact of record-wise fold creation discussed in Section 3.4.2, the classifier could have been biased to detect unique individual information, represented mainly by MFCC and PLP coefficients, rather than \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**AVCA**} & \multicolumn{4}{c}{**DARTH-VAT**} \\ \hline
**Corpus** & **Vowel** & **LSVM** & **PCA-** & **FS-** & **FS-** & **RFC** & **PCA-** & **FS-** & **LSVM** & **PCA-** & **FS-** & **RFC** & **PCA-** & **FS-** \\ \hline Neurovoz & /a/z/ & 52.8 & **62.5** & 53.4 & 72.2 & 69.9 & **73.9** & 50.6 & 55.1 & **56.3** & 71.6 & 63.1 & **75.6** \\  & /e/z/ & 57.2 & **60.7** & 56.6 & 71.7 & 70.3 & **73.1** & 53.8 & **62.1** & 57.9 & **74.5** & 73.8 & 72.4 \\  & /i/z/ & 61.8 & **70.0** & 58.2 & 77.6 & 73.5 & **78.8** & **59.4** & 57.1 & 57.1 & 79.4 & 70.6 & **81.8** \\  & /or/z/ & 53.5 & **60.4** & 59.8 & 72.2 & 64.5 & **73.4** & 51.5 & 52.7 & **53.3** & 74.6 & 58.6 & **76.3** \\  & /u/z/ & 56.4 & 61.8 & **62.7** & 73.6 & 72.7 & **75.5** & 50.9 & **62.7** & 59.1 & **83.6** & 71.8 & 80.9 \\ \hline ItalianPVS & /a/z/ & 93.9 & 90.9 & **98.0** & 99.0 & 89.9 & **99.0** & 50.5 & 55.6 & **84.8** & 99.0 & 72.7 & **100.0** \\  & /e/z/ & 77.8 & 85.9 & **93.9** & **99.0** & 94.9 & **99.0** & 53.5 & 55.6 & **74.7** & 98.0 & 77.8 & **99.0** \\  & /i/z/ & 90.9 & 90.9 & **93.9** & **99.0** & 96.0 & **99.0** & 50.5 & 54.5 & **92.9** & **99.0** & 69.7 & **99.0** \\  & /oz/ & 88.9 & 87.9 & **97.0** & **99.0** & 97.0 & 98.0 & 49.5 & 57.0 & **86.0** & 98.9 & 76.3 & **100.0** \\  & /u/z/ & 78.8 & 90.9 & **92.9** & **99.0** & 94.9 & 98.0 & 53.5 & 51.5 & **60.6** & 98.0 & 80.8 & **99.0** \\ \hline mPower & /a/z/ & 53.7 & **58.1** & 55.4 & **62.4** & 61.6 & 61.4 & 55.5 & 55.0 & **57.3** & **63.3** & 58.9 & 57.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Best classification accuracy (%) in the baseline (subject-wise) experiments after grid search. This scenario is described in Section 3.4.1. Highest accuracy per vowel, coefficient, classifier, feature reduction strategy, and corpus are in bold. Figure 7: Classification accuracy results using speaker-wise (blue) and record-wise (gray) fold creation methodologies when employing AVCA (**top**) and DARTH-VAT (**bottom**) feature extraction libraries. actual biomarkers or PD patterns. Detection accuracy in AVCA-related trials was generally higher than that from DARTH-VAT-related trials in the record-wise fold experiments. The reason could be that the coefficients obtained via the AVCA library contain more speaker information as there are two perceptual coefficient families: MFCC and PLP, whereas the DARTH-VAT only calculates MFCCs. Trials using the Neurovoz corpus increased the most in accuracy: only 2 out of 30 RF trials and 7 out of 30 SVC trials saw a decrease in accuracy from the baseline, though most differences are by no more than 3 absolute percentage points. The fact that both RFC and SVM classifiers perform better suggests that speaker information has an impact on classification and leads to overoptimistic results in experiments in which there are recordings from the same speakers in training and testing. In some instances, dimensionality reduction trials saw little to no increase in accuracy with respect to the baseline scenario.