## 4 Conclusions and Future Works  
This study aimed to use a deep learning-based SCR system to help dysarthric patients control mobile devices via speech. Our results showed that: (1) the speech feature of PPG can achieve better recognition performance than MFCC and (2) the proposed CNN-PPG SCR system can provide higher recognition accuracy than two classical SCR systems in this study; meanwhile, only a small model size is needed to compare the CNN-MFCC and ASR-based SCR systems. More specifically, the average accuracy of CNN-PPG achieved an acceptable performance (i.e., 93.49% recognition rate) in this study; therefore, the CNN-PPG can be applied in the SCR system to help dysarthric patients control a communication device using their speech commands, as shown in Figure 5. Specifically, the dysarthric patients can use combinations of 19 commands to select application software functions (e.g., YouTube, Facebook, and messaging). In addition, we plan to use natural language processing technology to provide automated options from the interlocutor's speech; meanwhile, these candidates' response sentences will be selected through the patient's 19 commands, thereby accelerating the patient's response rate. Finally, we plan to implement this proposed CNN-PPG in a mobile device to help dysarthric patients improve their communication quality in future studies. Figure 5: An example of the processed SCR system for dysarthric speakers. Users can maneuver through number commands to select the program they want to use or the help they need. The green area is the item that can be controlled by voice command. The proposed CNN-PPG system provided higher performance than two baseline SCR systems in a personalized application condition; however, the performance of the proposed system could be limited in general application (i.e., without asking the user to record the speech commands), because of the challenging the issues of individual variability and phonetic variation in the speech [10; 11; 12] of dysarthric patients. To overcome this challenge and further improve system benefits, two possible research directions appear. First, we will provide this system for free use to dysarthric patients and many patients' voice commands, with the users' consent. Next, the data obtained can, therefore, be used to retrain the CNN-PPG system to improve performance in a generalization application condition. Second, advanced deep learning technologies [50; 76; 77; 78; 79], such as few-shots audio classification, 1D/2D CNN audio temporal integration, semi-supervised speaker diarization deep neural embeddings, convolutional 2D audio stream management, and data augmentation, can be used to further improve the performance of the proposed CNN-PPG SCR system in future studies. Conceptualization, Y.-H.L., G.-M.H., C.-YC., and Y.-Y.L. ; Methodology, Y.-H.L. ; Software, W.C.C., W.-Z.Z., and Y.-H.H. ; Validation, Y.-Y.L., J.-Y.H., and Y.-H.L. ; Formal Analysis, W.-Z.Z. ; Investigation, Y.-Y.L. and Y.-H.L. ; Resources, G.-M.H., C.-YC., and Y.-H.L. ; Data Curation, W.-Z.Z. ; Writing--Original Draft Preparation, Y.-H.L. and Y.-Y.L. ; Writing--Review and Editing, Y.-Y.L. and Y.-H.L. ; Visualization, Y.-H.H. ; Supervision, Y.-H.L. ; Project Administration, J.-Y.H. and Y.-H.L. ; Funding Acquisition, Y.-H.L., G.-M.H., and C.-YC. All authors have read and agreed to the published version of the manuscript. This study was supported by funds awarded by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2218-E-010-004, and by industry-academia cooperation project funding from APrevent Medical (107J042). This investigation was approved by the Ethics Committee of Taipei Medical University--Joint Institutional Board (N201607030) and was performed according to the principles and policies of the Declaration of Helsinki. Informed Consent Statement: Informed written consent for participation was gained from all participants. Data Availability Statement: Data are presented in the manuscript; further information is available upon request data.