# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 4 Results and Discussion  
In this section, results for each one of the five steps mentioned in the previous section were presented. As results contain classification accuracies from multiple classifiers, box plots were used to show their distribution. MFCCs with block segmentation with various block sizes ranging from 10 ms to 40 ms were extracted and utilized for PD/HC classification. The result did not demonstrate any significant impact due to block sizes and for brevity, only results from a block size of 25 ms were used whenever block processing results were discussed to be investigated from hereon. The results from PS and block processing (25 ms block size, 50% overlap) with original labels and randomly assigned labels along with their relative differences were used. The outcomes from MFCCs using block and PS segmentations for both genders in Database 1 using holdout validation are provided in Figure 5. It was observed that both segmentation methods performed similarly across all genders. These results indicated that except for females with block processing, the PS segmentation led to comparable or even greater degradation in all other cases. Notably, some of the classifiers exhibited negative values for percentage decrease, indicating better classifications with randomized labels. Upon closer examination, it was revealed that classifiers with fine kernels were primarily responsible for such effects. These classifiers were also found to be prone to overfitting issues, as discussed in detail later in this paper. Overall, block segmentation yielded superior accuracies compared to PS segmentation when MFCCs are used. When classifiers were trained using data with random labels, performance decreased in all cases as expected. The relative accuracy reduction due to random label assignment is shown in Figure 6.  
The relative reduction in accuracy was also more pronounced in the case of block segmentation for females. In males, both segmentation methods were comparable when data was labeled randomly. In conclusion, it was observed that when trained with random labels, the degradation in accuracy was more pronounced, highlighting a lack of robustness in the classifiers while using block segmentation. Figure 5: Classification results with different segmentations using original labels (MFCCs from Database 1). The comparison between feature types was made using classification accuracies with original labels and the relative decline in performance due to random label assignment. The performance comparison between MFCCs from PS segmentation and PSFs using Database 1 for both genders is shown in Figure 7. We observe that in female, PSFs has better median accuracies than MFCCs. The significantly higher relative reduction in performance for PSFs along with higher classification accuracies for MFCC as depicted in Figure 6, indicates that MFCCs contain more speaker-identifiable information while PSFs exhibit a higher capability to capture the impact of PD on vocalic dynamics. Conversely, PSFs exhibited a higher capability to capture the impact of PD on vocalic dynamics compared to MFCCs. The percentage reduction in classification performance with original labels and randomly assigned labels was consistently higher for PSFs than MFCCs in all cases, as demonstrated in Figure 8. In conclusion, these findings suggest that PSFs are more effective than MFCCs in containing the effects of PD, while MFCCs are better suited for speaker  
Figure 6: Percentage reduction in classification performance with original and random labels (MFCCs from Database 1). Figure 7: Classification performance comparison between MFCCs and PSFs (Database 1). identification and verification applications. As PSFs are extracted using PS segmentation, between block processing and PS segmentation, the latter becomes the optimal choice. Next, to recognize the optimal set of classifiers, each one of the 17 classifiers was evaluated using two different metrics:  
1. Overfit factors are displayed in Table 2;
2.