## 3 Results and Discussion
### Recognition Performance of Each SCR System  
Figure 3 shows the accuracy of the three models with different model sizes (related to the parameter numbers). The highest accuracy of the CNN-MFCC system was 65.67% (i.e., total parameter number 917,173), while the parameter numbers ranged from 471,169 to 917,173 and showed similar accuracy. The second model, the CNN-PPG system, showed  
Figure 2: The \(t\)-SNE analysis of (**A**) CNN–MFCC model and (**B**) CNN–PPG model. In the CNN–PPG groups, the \(t\)-SNE of 19 commands were separate, distinct from the CNN–MFCC groups. Note: the labels “01” to “19” are the commands of close, up, down, previous, next, in, out, left, right, home, one, two, three, four, five, six seven, eight, and nine, respectively. the best accuracy performance at 470,039 parameters, achieving 93.49%, while parameter numbers from 469,256 to 470,387 showed similar accuracy. The third model, ASR, showed the best performance at 89.59% with the parameter number 427,184, and the parameter numbers from 237,104 to 509,360 showed similar accuracy. Figure 4 shows the average recognition rate of a test repeated 10 times in different models: CNN-MFCC, CNN-PPG, and ASR-based. The average accuracy \(\pm\) standard deviation of the CNN-MFCC system was 65.67% \(\pm\) 3.9%, that of the CNN-PPG system was 93.49% \(\pm\) 2.4%, and that of the ASR-based system was 89.63% \(\pm\) 5.9%. The results indicate that CNN-PPG had the highest accuracy score and more stable performance compared with the other two systems. Hence, the PPG features are more robust than MFCC features in the CNN deep learning model. PPG features achieved higher recognition performance than MFCC in this study, which is consistent with previous studies [46]. The PPG features extract phone probability for each input frame; meanwhile, the monophone was used in the study. Therefore, it provided better performance than the well-known MFCC features. In addition, the results of this study show that the ASR-based SCR system provided better recognition performance than CNN-MFCC, though slightly lower than the CNN-PPG method. Thus, while previous studies indicated that the ASR-based SCR system is a gold standard in the application of the SCR task for dysarthric patients [21], the ASR system did not provide the best performance in this study. Furthermore, we investigated the cross-validation results ten times in each model. For the ASR-based SCR system, the best accuracy was 96.4%, in contrast to the best accuracy of CNN-PPG during ten times cross-validation history, 97.6%. The best accuracy of the two models was similar, but that of the proposed CNN-PPG was higher, due to the limited training data to decrease the ability of the ASR-based SCR system. Therefore, while on average, the ASR-based SCR system did not provide the best performance in this task, if a larger training set was available, the ASR-based system might provide better performance. Figure 3: Comparison of difference model size of the three used models. The x-axis denotes the parameter numbers, and the y-axis denotes the recognition rate of speech command from dysarthric patients. Table 1 details the 10 experimental results (i.e., 10 repetitions/times) of these three SCR systems in the training and application phases. Note that each experiment was independent and separated and repeated 10 times. From the results of Table 1, we observed that there were no overfit [67,68] issues in the proposed CNN-PPG, because the performance between training and application phases was similar. In contrast, the CNN-MFCC system had the issue of overfit for all test times, with over 30% error between training and application phases. For the ASR-based system, there were also sometimes overfit issues in this study. These results indicated that the proposed CNN-PPG system can provide more stable performance than baseline systems for dysarthric patients in real application.