###### Abstract  
Parkinson's disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have significantly enhanced PD diagnosis through the analysis of speech data. Nevertheless, the progress of research is restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns. The goal of this systematic review is to explore the current landscape of speech-based DL approaches for PD classification, based on 33 scientific works published between January 2020 and March 2024. We discuss their available resources, capabilities, and potential limitations, and issues related to bias, explainability, and privacy. Furthermore, this review provides an overview of publicly accessible speech-based datasets and open-source material for PD. The DL approaches identified are categorized into end-to-end (E2E) learning, transfer learning (TL), and deep acoustic feature extraction (DAFE). Among E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, though Transformers are increasingly popular. E2E approaches face challenges such as limited data and computational resources, especially with Transformers. TL addresses these issues by providing more robust PD diagnosis and better generalizability across languages. DAFE aims to improve the explainability and interpretability of results by examining the specific effects of deep features on both other DL approaches and more traditional machine learning (ML) methods. However, it often underperforms compared to E2E and TL approaches. Parkinson's disease; speech classification; deep learning; speech processing; speech diagnosis; Artificial Intelligence; end-to-end; transfer learning; deep acoustic feature extraction +
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Speech classification generally aims to distinguish between people with Parkinson's (PWP) and healthy controls (HC) [10]. Using speech-processing techniques, acoustic features (also known as acoustic biomarkers) can be extracted from speech signals to capture these changes. Classical features include fundamental frequency (F0), jitter, shimmer, Mel Frequency Cepstral Coefficients (MFCCs), and formant frequencies [1]. However, recent studies in the literature employ deep acoustic feature extraction (DAFE) from end-to-end (E2E) systems, auto-encoders, or pre-trained models [11; 12; 13]. Machine learning (ML) approaches require manual feature extraction, while deep learning (DL) methods automatically extract deep features [14]. Additionally, these features tend to be more abstract and robust [14], potentially leading to a more accurate diagnosis of PD. These features can be used not only in the final layers of a deep learning model but also in more traditional machine learning approaches, offering more interpretable results, albeit with potentially lower performance. Until 2016, traditional methods such as Gaussian mixture models (GMMs) and hidden Markov models (HMMs) were the standard ML approach for speech processing tasks [15]. GMMs and HMMs typically use an acoustic, language, and pronunciation model. However, training each of these models separately and aligning their training data can be labor intensive. Recently, DL has emerged as the leading computational method in AI and its subfield, ML, delivering outstanding outcomes on a variety of complex cognitive tasks, and sometimes even outperforming humans [15; 16]. In healthcare, DL has proven its potential not only in PD classification but in other clinical areas [17] such as cancer diagnosis [18], drug discovery [19], and precision medicine [20]. Studies that have been published in most recent years within the realm of speech-based DL approaches for PD classification mainly focus on E2E learning [21; 22; 23] and transfer learning (TL) [24; 25; 26]. Recently, there has been a growing interest in privacy-related issues in speech-based AI research [27; 28].