## 5 Discussion  
Despite extensive research of the potential for acoustic analysis in the classification of PD voices, the methods used, as well as parameters considered, vary immensely among studies. The standardization of study protocols as well as reproducibility studies are lacking. The acoustic speech signal carries a massive amount of information. Nevertheless, the direct link between voice production and acoustics has not been explicitly studied [87]. Moreover, the pathophysiology of speech production itself is not yet fully understood in PD [88]. Speech function is well known to deteriorate with disease progression up to unintelligible levels in the latest stages. However, only a fraction of studies analyze speech in the early phase of PD. The phonetics of spoken languages differ in some aspects and are shared in others. Therefore, some acoustic parameters may have similar values and meanings in different populations [89], while others are not to be universalized directly. Acoustic studies in different languages may shed some light on the topic and the achieved results. [PERSON] [90] declared his group the first to successfully implement the cross-corpora trials for AI PD detection. They used databases of Czech and two dialects of Spanish speakers and only used diadochokinetic speech task for training with one and testing with another database, as these utterances were supposed to not differ across the databases. Our approach of a cross-corpus trial was driven by the fact that Lithuanian is a language, spoken by as little as 3 million native speakers, and is largely underinvestigated.  
Considering the sample size, losing part of the subjects to testing was chosen not an option. The bold approach to test on other language speech samples, was encouraged by the work of [PERSON] et al. [89] who had implemented a composite dysarthria score for testing early PD and RBD speakers of 7 different languages with comparable results and an overall AUC of 0.8. Therefore, It can be considered that PD differently affects certain phonetic groups, with those requiring the narrowest contact between the articulators (such as fricatives), being influenced the most, correlating with results shown by Ref. [90]. Still, naturally, a substantial effect on other phonetic groups cannot be overlooked as the disease has been shown to impair the articulatory sequence as a whole [90]. A comparison of the effectiveness of different deep learning approaches is offered in Table 4.  
With the above-mentioned studies, to date there is no established universal approach to study speech impairment in PD. Speech acoustic analysis has not been included in the latest Movement disorder society (MDS) criteria for PD [95; 96], nor MDS Research Criteria for Prodromal Parkinson's Disease [97]. As was stated by [PERSON] in his editorial for [98], automatic systems to assess PD will benefit from new knowledge generated in the research area to develop more accurate and robust systems, as was also shown in our experimental overview.  
Finally, the fact that hypokinetic dysarthria is not the integral symptom of PD, with an estimated prevalence of up to 90% of cases with rising expression in the latest stages [99], the accuracy of classification obtained on both the Italian and Lithuanian corpora is satisfactory from a medical perspective. Furthermore, our proposed approach reaches an accuracy not markedly lower in the cross corpora-cross linguistic trial, indicating the generalizability of the method to different populations and recording conditions.