Parkinson’s Disease (PD) is a progressive neurodegenerative disorder for which early diagnosis significantly improves patient outcomes. Vocal impairments—such as altered pitch, articulation, rhythm, and voice clarity—emerge in preclinical stages and provide non-invasive biomarkers for early detection. In this study, we present a comprehensive evaluation of machine learning models applied to vocal biomarkers recorded in 2025, incorporating self-supervised speech representations, interpretable deep learning, and domain-adaptive architectures to enhance generalization across languages and populations. Leveraging contemporary datasets spanning multiple languages and at-home recordings, our models—ranging from traditional classifiers like Random Forest and SVM to advanced deep architectures—demonstrate high sensitivity and specificity, with cross-lingual performance exceeding 90% in several cases. We also introduce novel interpretability techniques that highlight the vocal segments most predictive of PD, enabling clinicians to understand underlying neuromuscular impairments. Our findings support the viability of deploying accessible, voice-based diagnostic tools—via smartphones or smart home devices—for early screening, thereby contributing to timely intervention strategies.

Parkinson’s Disease, Vocal Biomarkers, Speech Analysis, Early Diagnosis, Machine Learning, Deep Learning, Signal Processing, Digital Health, Neurodegenerative Disorders, Telemedicine.

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