## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** &
\begin{tabular}{c} **Group of** \\ **LDS** \\ \end{tabular} & **LLD** & **Functionals** & **Group** \\ \hline mfcc\_sma\_de[3]\_upleveltime25 & Cepstral & MFCC & Up-Level Time 25\% & Temporal \\ mfcc\_sma\_de[14]\_meanFallingSlope & Cepstral & MFCC & Mean of & Peaks \\ eudSpec\_Rfilt\_sma\_de[14]\_quartile3 & Prosodic & RASTA-style filtered & Quartile 3 & Percentiles \\ eudSpec\_Rfilt\_sma\_de[3]\_quartile3 & Prosodic & RASTA-style filtered & Quartile 3 & Percentiles \\ pcm\_RMSenergy\_sma\_de\_kurtosis & Prosodic & RMS Energy & Kurtosis & Moments \\ voicingFinalUnclipped\_sma\_de\_centroid & Voice Quality & Voicing & Centroid & Temporal \\ pcm\_fft\_Mag\_spectralSlope\_sma\_de\_kurtosis & Spectral & Spectral Slope & Kurtosis & Moments \\ logHNR\_sma\_skewness & Voice Quality & HNR & Skewness & Moments \\ pcm\_RMSenergy\_sma\_de\_upleveltime25 & Prosodic & RMS Energy & Up-Level Time 25\% & Temporal \\ eudSpec\_Rfilt\_sma[4]\_lpc4 & Prosodic & ZCR & Coefficient 3 & Modulation \\ pcm\_zcr\_sma\_lpc3 & Prosodic & ZCR & Linear Prediction & \\ eudSpec\_Rfilt\_sma[12]\_segLenStddev & Prosodic & RASTA-style filtered & & Coefficient 4 & Modulation \\ ecdSpec\_Rfilt\_sma[12]\_segLenStddev & Prosodic & RASTA-style filtered & & Standard Deviation & Moments \\ pcm\_fft\_Mag\_spectralEntropy\_sma\_peakRangeAbs & Spectral & Spectral Entropy & Amplitude Range & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top performing acoustic features, selected by the GA wrapper towards the NB classifier for the sentence vocal task. The names are according to the Compare 2016 nomenclature. Figure 3: Example of ROC curves for the NB classifier (all selectors), PD detection versus the other classes. (**a**) Vowel/e/ task; (**b**) sentence task. Figure 4 illustrates the respective acoustic groups of the selected features across all methods, vocal tests, and classifiers. The groups were spectral, cepstral, prosodic (energy related), and voice quality [37; 38]. Spectral and prosodic groups were dominant across all feature selection methods whereas cepstral and voice quality groups varied in availability from one method to another. A further insight was made into the selection of top performing features by creating a radar chart, displayed in Figure 5. This chart was created using mean values of the PD and ASD features and normalized to its respective healthy ones. The chart shows an acoustic signature for both PD and ASD against the healthy voices. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Task** & **Classifier** & \begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular}  & **ACC** & \begin{tabular}{c} **TP** \\ **Rate** \\ \end{tabular}  & **FP Rate** & **Precision** & **Recall** & \begin{tabular}{c} **F-** \\ **Measure** \\ \end{tabular}  & **MCC** & \ \begin{tabular}{c} **ROC** \\ **Area** \\ \end{tabular}  & \begin{tabular}{c} **PRC** \\ **Area** \\ \end{tabular}  \\ \hline \multirow{8}{*}{Vowel/e/} & \multirow{3}{*}{NB} & Corr. & 91.94\% & 0.919 & 0.042 & 0.92 & 0.919 & 0.919 & 0.88 & 0.986 & 0.976 \\ \cline{2-11}  & & IG & 92.47\% & 0.925 & 0.038 & 0.925 & 0.925 & 0.924 & 0.887 & 0.984 & 0.974 \\ \cline{2-11}  & & GR & 93.55\% & 0.935 & 0.035 & 0.936 & 0.935 & 0.935 & 0.902 & 0.985 & 0.975 \\ \cline{2-11}  & & GA & **95.70\%** & **0.957** & **0.022** & **0.957** & **0.957** & **0.957** & **0.935** & **0.994** & **0.99** \\ \cline{2-11}  & & Corr. & 87.10\% & 0.871 & 0.067 & 0.872 & 0.871 & 0.87 & 0.806 & 0.97 & 0.953 \\ \cline{2-11}  & & IG & 88.17\% & 0.882 & 0.061 & 0.882 & 0.882 & 0.881 & 0.821 & 0.974 & 0.959 \\ \cline{2-11}  & & GR & 87.10\% & 0.871 & 0.068 & 0.871 & 0.871 & 0.87 & 0.806 & 0.97 & 0.95 \\ \cline{2-11}  & & GA & **93.55\%** & **0.935** & **0.033** & **0.936** & **0.935** & **0.935** & **0.903** & **0.983** & **0.972** \\ \cline{2-11}  & & Corr.