## 4 Results & Discussion
### Baseline Results  
Table 12 includes the classification accuracy results of the baseline of the study. In this scenario, all the analyzed data sets are class-balanced and the PwPD and HC classes are age- and gender-matched. In these, and in most of the trials of this study, the ItalianPVS corpus provided the best results overall, with accuracy near 100% when using RFC classifiers. This corresponds to more perceptible dysphornia in PwPD, according to Figure 5. In each experiment, RFCs tend to have much higher accuracy than SVMs. In RFC trials on the Neurovoz corpus, the vowels /i:/ and /u:/ tend to perform the best. In that corpus, the vowel /a:/ frequently provides worse results, and it never provides the highest accuracy when compared to the rest of the trials. One possible reason could be that the production of the open-back vowel /a:/ requires a more relaxed position of tongue than other tighter, extreme vowels such as the back-close /u:/ and the front-close /i:/ [63]. However, another possible reason when analyzing results of the vowel /u:/ could be that this vowel contains fewer recordings, as indicated in Table 1. As discussed in Section 3.4.4, a smaller number of observations might lead to higher (overoptimistic) accuracy when employing very large feature vectors. In ItalianPVS trials, the RFC experiments also tend to provide the highest detection accuracy, but the differences between RFC classifiers are lower, especially when using the AVCA-based feature extraction algorithms.  
The coefficients obtained using the AVCA toolbox provide similar results to those calculated using the DARTH-VAT toolbox when using RFC classifiers. However, AVCA-related trials tend to provide higher accuracy than those from DARTH-VAT when employing SVM classification schemes. For instance, in the ItalianPVS experiments, SVM accuracy ranges from 77.8-93.9% using AVCA versus 49.5-53.5% using DARTH-VAT. The differences in the other corpora are much subtler but still present nonetheless. These differences may be attributed to the PLP and Modulation Spectrum (MS) features from AVCA, which are not present in DARTH-VAT.  
In most experiments using the mPower corpus, vowel /a:/ classification accuracy is lower than in the other corpora by as much as 16.4% (absolute). In order to ensure that the mPower subset is representative, we obtained six _parallel subsets_ by randomly selecting speakers from the reference subset (\(S_{R}\)) and obtaining the same age and gender statistics indicated in Table 6. Table 12 includes the mean and variance of accuracy from repeating the grid search with each of the six subsets. These results are supported by the GRBAS results of this corpus as well: there is a notably smaller difference of perceived dysphornia between those with and without PD. Although the perceptual evaluation reveals a less distinguishable dysphornia, other factors could be influencing these results, such as the differences in microphones, background noise, and acoustic conditions in the mPower corpus collection method, as suggested in previous studies [64,65].  
Using dimensionality reduction techniques increased the SVM classifier accuracy in most experiments. On the other hand, feature selection using SelectKBest on RFC classifiers led to ambivalent results, while PCA lowered the accuracy in every experiment. In fact, given that the decision trees included in RFC classifiers are based on an algorithm that finds the features that better differentiate between classes, as in many feature selection algorithms, using dimensionality reduction techniques might be unuseful or redundant when employing these types of ensemble algorithms.