Multi-Modal Decentralized Hybrid Learning for Early Parkinson's Detection Using Voice Biomarkers and Contrastive Speech Embeddings.

Millions worldwide are affected by Parkinson's disease, with the World Health Organization highlighting its growing prevalence. Early neuromotor speech impairments make voice analysis a promising tool for detecting Parkinson's, aided by advances in deep speech embeddings. However, existing approaches often rely on either handcrafted acoustic features or opaque deep representations, limiting diagnostic performance and interoperability. To address this, we propose a multi-modal decentralized hybrid learning framework that combines structured voice biomarkers from the UCI Parkinson's dataset (195 sustained-phonation samples from 31 subjects) with contrastive speech embeddings derived from the DAIC-WOZ corpus (189 interview recordings originally collected for depression detection) using Wav2Vec 2.0. This system employs an early fusion strategy followed by a dense neural classifier optimized for binary classification. By integrating both clinically interpretable and semantically rich features, the model captures complementary phonatory and affective patterns relevant to early-stage Parkinson's detection. Extensive evaluation demonstrates that the proposed method achieves an accuracy of 96.2% and an AUC of 97.1%, outperforming unimodal and baseline fusion models. SHAP-based analysis confirms that a subset of features have disproportionately high discriminative value, enhancing interpretability. Overall, the proposed framework establishes a promising pathway toward data-driven, non-invasive screening for neurodegenerative conditions through voice analysis.