## 6 Discussion
### Findings and Implications  
Regarding _RQ1_. Which speech-based DL approaches are considered for PD classification?_ this systematic review has categorized and described the most recent scientific works in the literature about speech-based DL approaches for PD classification into three main categories, E2E, TL, and DAFE. In particular, CNN architectures under the E2E learning approach are the most popular speech-based approach for PD classification. Most of the E2E-based studies described in Section 3 show that CNNs can consistently outperform other architectures, such as LSTMs and hybrid CNN-LSTMs. However, in recent years, the Transformer architecture has gained in popularity and is able to compete with CNNs in terms of performance. As for our _RQ2_. To what extent can speech-based DL approaches classify PD?_ whereas CNNs tend to focus more on local areas in the spectrogram, Transformers show more widespread patterns [51]. [PERSON] et al. [52] describe that, intuitively, the features related to PD are not local and that it can, therefore, be expected that Transformers will outperform CNNs and increasingly dominate the field of speech-based PD classification in the following years. However, thus far, Transformers have not been able to outperform CNNs by a significant margin. Additionally, Transformers typically require extensive training data, which presents a challenge because PD speech datasets are scarce and tend to be relatively small. This limitation could hinder the growing popularity of Transformer-based approaches. [PERSON] et al. [50] attributed their unsuccessful attempt to the insufficient number of subjects available for each severity stage identified by the H&Y scale, which consists of five categories. For instance, they had only two subjectsfor stage 1 and one subject for stage 5. Reducing the number of stages could potentially alleviate this issue as shown in [PERSON] et al. [51]. They demonstrate that having fewer classes (two) might result in a larger number of subjects per class, thereby facilitating the learning and prediction of PD severity by a Transformer model. Although not all attempts to predict PD severity have been successful, these efforts remain relevant, particularly for monitoring disease progression [71]. Datasets lacking information about PD severity of the patients prevent researchers from distinguishing between different stages of PD and more advanced PD research. Therefore, it is advisable to enrich datasets with such information. As for the second DL approach, TL (explained in Section 4) has been shown to mitigate the issue of data availability. Therefore, there has been a recent increase in the number of studies utilizing TL for PD diagnosis. Most of the studies identified in this review are cross-lingual in nature, although some languages seem to transfer better than others. Several studies have shown that Spanish usually serves as a better base language for a dataset than, for instance, German or Czech (see [25; 63; 64]). One possible explanation for this is that PD is diagnosed most accurately in Spanish in the first place. In other words, without applying any form of TL, PD is diagnosed more accurately in Spanish compared to Czech or German [63]. Another reason is that the datasets for different languages were not elaborated under the same conditions, such as the severity stage of the disease. In more advanced stages, PD diagnosis might be easier to recognize than in earlier stages. In [64], German and Spanish patients were in more advanced stages than Czech patients. This could explain why we are better able to diagnose Spanish patients in the first place, and subsequently, why Spanish transfers better than other languages. Nonetheless, PD prediction is more accurate for Spanish than German patients while both are in more advanced stages of the disease. Therefore, it might not be entirely unreasonable to think that certain languages might transfer better than others.