###### Abstract
Scientists increasingly question the reliability of speech-based AI systems, becoming more aware of concerns and risks over the trustworthiness of these systems since speech can be used as a biometric identifier to identify individuals and obtain information about their health status [27]. Datasets containing the speech of patients with PD are sensitive in nature, and safety and fairness (e.g., without bias) need to be ensured [27]; therefore, the number of such datasets that are publicly available is limited (see Table A1) as is the open-source code for the research experiments (see Table A3). This review presents an overview of the most recent, innovative speech-based DL approaches for PD classification as of March 2024. Previously, other authors have reviewed different approaches to PD classification from various perspectives. [PERSON] et al. [1] summarized the literature from 2010 to 2021 related to voice and speech ML and DL approaches for PD classification and the assessment of its severity. [PERSON] et al. [10] presented a review of papers until 2021 on ML and DL methods that are not only related to speech but also to physiological signals, such as electroencephalogram (EEG) and neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and single-photon emission computed tomography (SPECT). In this review, we focus on the most recent (from 2020 to March 2024) innovative DL-based approaches related to speech, which, to the best of our knowledge, have not yet been covered in previous literature reviews, guided by the overarching research question: _What is the current landscape of speech-based DL approaches for PD classification?_ which has been further subdivided into the following:  
**RQ1. **: _Which recent speech-based DL approaches are being considered for PD classification?_
**RQ2. **: _To what extent can speech-based DL approaches classify PD?_
**RQ3. **: _What are the issues related to bias, explainability, and privacy of speech-based DL approaches for PD classification?_  
The contributions of this systematic literature review are as follows:  
1. This review covers the most recent speech-based DL approaches for PD classification, until March 2024, for which the available resources, capabilities, and potential limitations are discussed. The review explicitly focuses on speech-based data and DL approaches. 2. This review discusses issues relating to bias, explainability, and privacy associated with speech-based DL approaches for PD classification. 3. This review includes an overview of publicly available speech-based datasets and open-source resources for PD classification, up to March 2024. The rest of the paper is structured as follows. In Section 2, the eligibility criteria, exclusion criteria, and search procedure are described. In Section 3, E2E learning approaches based on applied Deep Neural Network (DNN) architectures, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers, are discussed. In Section 4, some of the most recent TL techniques are explained, and in Section 5, we describe approaches that use DAFE. Section 6 discusses the results according to the RQs and reflects on potential implications for researchers of the theoretical and practical findings. Section 7 outlines the conclusions of this work. Finally, in the first two appendices, we show a table listing the publicly available speech-based datasets for PD research (Table A1) and an overview of scientific works performing speech classification for PD following a DL approach (Table A2). In the last appendix, we present a table containing the open-source research code available from the scientific works included in this review (Table A3).