## 6 Discussion
### Findings and Implications
While some languages transfer better than others, the growing number of cross-lingual studies is significant because a cross-lingual approach can lead to more PD classification and enhance generalizability to other languages. Ideally, a system developed for one language should be usable by speakers of other languages as well. Developing separate systems for each language is impractical, so creating a single system that is widely applicable would be highly beneficial. In [25; 57], the Spanish dataset was found to be the most effective base language for development, suggesting that systems should be developed in Spanish and then adapted to other languages. The best performance reported was 77.3% in [25], 78.85% in [57], and around 90% in [64]. In [5], [PERSON] et al. demonstrated that fine-tuning on multiple languages instead of just one improves performance. This suggests that fine-tuning models on several languages could enhance both robustness and applicability. Future research should explore which languages besides Spanish could serve as effective base languages. Current studies focus on Czech, English, German, or Italian (and Spanish), but more attention should be given to other linguistic and cultural contexts, as PD is a global disease affecting diverse populations. However, cross-lingual studies use different datasets from different languages. These datasets may contain speech from different kinds of speech tasks, e.g., diadochokinetic (DDK) tasks, reading full sentences, and free speech. Therefore, the performance increase we often observe for cross-lingual studies might not only be attributed to the use of different languages but also to the use of different types of speech tasks. Nevertheless, using TL among different languages still proves to be useful since it can enhance the applicability of such systems in the real world. The same principle applies to other neurodegenerative diseases beyond PD: having a single diagnostic system that can be used for multiple diseases greatly enhances its applicability. Consequently, it is expected that future scientific research will increasingly be cross-lingual and cross-pathological. In Section 5, it was shown that not all recent work focuses solely on E2E and TL approaches. DAFE (the third category of approaches) can be used not only for DL but also as input for more traditional ML methods to classify between PVP and HC and obtain a better interpretation and explainability of the results. For this approach, cross lingual studies are becoming more common, which is similar to what we observed for the methodologies discussed in Sections 3 and 4. This underlines the potential of cross-lingual studies in terms of interpretability, explainability, and generalization. Although studies using DAFE can achieve a high performance of over 90% [12], and even obtain better results than traditional approaches like MFCCs [13], DAFE still underperforms compared to E2E and TL approaches. Therefore, it is unlikely that DAFE will dominate the field in the near future in terms of performance. However, while DAFE approaches may not be able to compete with the other two approaches, it can help to describe the explainability and interpretability of the results, an issue that should receive more attention in the future. Finally, we cannot draw a definitive conclusion about which type of speech task is most suitable for diagnosing PD. Some of the studies reviewed in this work (e.g., [26]) achieved good performance with sustained vowels, while others performed well using sentences, such as [21]. In 2013, [PERSON] et al. [72] suggested that sustained vowels contain more PD-discriminative information than words and sentences. However, the best accuracy using sustained vowels was 85%, while more recent studies (e.g., [51]) have shown better performance with both sustained vowels and other types of speech. Since [PERSON] et al. 's study [72] was conducted over a decade ago, more traditional ML methods were used, rather than the DL methods that are of greater interest today. [PERSON] et al.