## 7 Conclusions  
The goal of this work was to provide an overview of innovative speech-based DL approaches for PD classification. Given the rapid advancements in this field, we prioritized the most recent and innovative approaches from 33 research works published between 2020 and March 2024. This systematic literature review makes a significant contribution to the field by addressing a gap in which no comprehensive systematic review has previously been conducted. In particular, it provides a detailed overview of innovative speech-based DL approaches for PD classification, divided into three distinct DL categories. This review also discusses their available open-source resources, capabilities, and potential limitations, and highlights issues related to bias, explainability, and privacy. It also presents a list of publicly available speech-based datasets for PD. Most of the reviewed studies used private speech-based PD datasets and followed an E2E learning approach, particularly employing CNN architectures, which generally achieve the highest PD classification accuracy. However, the Transformer architecture is becoming competitive in performance; however, it requires extensive training data. Regardless, studies using TL, particularly cross-lingual, showed enhanced diagnostic system robustness and applicability across different languages, although some languages transfer better than others. While approaches utilizing DAFE achieved better explainability and reasonable performance, they often underperformed compared to E2E and TL approaches. Finally, we have analyzed bias, explainability, and privacy risks of the three speech-based DL approaches. We recommend that future studies focus on speech-based deep learning methods that minimize bias and enhance privacy and XAI, while still achieving state-of-the-art performance. Furthermore, increasing transparency by making open-source resources available will be crucial for the field. Conceptualization, Lv.G. and C.T.-G.; methodology, Lv.G. ; formal analysis, Lv.G. and C.T.-G.; investigation, Lv.G. and C.T.-G.; resources, Lv.G. ; writing--original draft preparation, Lv.G. and C.T.-G.; writing--review and editing, Lv.G. and C.T.-G.; visualization, Lv.G. and C.T.-G.; supervision, C.T.-G.; project administration, C.T.-G.; funding acquisition, C.T.-G. All authors have read and agreed to the published version of the manuscript. This research was funded by the NWO research programme NGF AiNed Fellowship Grants under the project Responsible AI for Voice Diagnostics (RAIVD)--grant number NGF.1607.22.013. Not applicable. We would like to thank the RAIVD team, who helped with insightful ideas and suggestions for the final version of this work. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
1D One-dimensional  
2D Two-dimensional  
ADCNN Acoustic Deep Convolutional Neural Network  
ADNN Acoustic Deep Neural Network  
ADRNN Acoustic Deep Recurrent Neural Network  
AI Artificial Intelligence  
ALS Amyotrophic Lateral Sclerosis  
AUC Area Under the Curve  
CNN Convolutional Neural Network  
DAFE Deep Acoustic Feature Extraction  
DDK Diadochokinetic  
DIDK monosyllabic targets  
DL Deep learning  
DNN Deep Neural Network  
DT Decision Tree  
E2E End-to-end  
EEG Electroencephalogram  
EGSAE Embedded Stack Group Sparse Auto-encoder  
F Female  
F0 Fundamental frequency  
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* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Convolutional Neural Network Ensemble for Parkinson's Disease Detection from Voice Recordings. _Comput. Biol. Med._**2022**, _141_, 105021. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Speech and Language Therapy Treatment on Hypokinetic Dysarthria in Parkinson Disease: Systematic Review and Meta-Analysis. _Clin. Rehabil._**2021**, _35_, 639-655. [CrossRef]
* _[PERSON] et al.