Motor speech disorders in patients with Parkinson’s disease (PD), collectively referred to as hypokinetic dysarthria, are the early markers of the disease. Acoustic speech features are, therefore, suitable digital biomarkers for the diagnosis and monitoring of this pathological phenomenon. At the same time, it is clear that language plays an essential role in using these features to classify speech successfully into healthy and dysarthric one. This paper focuses on the multilingual analysis of the currently used and newly proposed acoustic speech features and their use in the supportive diagnosis of PD. The goal is to explore digital speech biomarkers of PD, focusing on their language independence and high discrimination power using statistical analysis and machine learning techniques. Thirty-three acoustic features of Czech, American, Israeli, Columbian and Italian PD patients and healthy controls were analyzed using correlation and statistical tests, descriptive statistics, and the XGBoost classifier with posterior explanation by features importances and Shapley values. The features quantifying the prominence of the second formant, monopitch, and the lower number of pauses detected during text reading show the best results, both by statistical analysis and machine learning. Classification accuracies range from 67% to 85%, depending on the language. The paper introduces the concept of language robustness as a quality in which a feature behaves the same, independently of the language. By using this new concept along with other metrics, it proposes several digital speech biomarkers that have the potential to be language-independent with high discrimination power.

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