Towards a Generalizable Speech Marker for Parkinson's Disease Diagnosis

2025-01-07

AI Summary

To address the need for non-invasive early screening of Parkinson’s disease (PD), this study proposes a cross-lingual, cross-population voice-based biomarker modeling framework. First, the HuBERT model is self-supervised pre-trained on elderly speech data to capture aging-related acoustic features. Subsequently, multilingual (English, Italian, Spanish) domain adaptation is integrated to enhance generalizability and clinical transferability in real-world settings. This work represents the first integration of aging-aware self-supervised pre-training with multilingual domain adaptation, substantially improving the robustness of voice biomarkers for PD detection. Evaluated on four publicly available PD voice datasets, the model achieves an average specificity of 92.1% and sensitivity of 91.2%. The approach enables low-cost, large-scale, and contactless PD screening, demonstrating strong potential for clinical deployment.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Parkinson's Disease (PD) is a neurodegenerative disorder characterized by motor symptoms, including altered voice production in the early stages. Early diagnosis is crucial not only to improve PD patients' quality of life but also to enhance the efficacy of potential disease-modifying therapies during early neurodegeneration, a window often missed by current diagnostic tools. In this paper, we propose a more generalizable approach to PD recognition through domain adaptation and self-supervised learning. We demonstrate the generalization capabilities of the proposed approach across diverse datasets in different languages. Our approach leverages HuBERT, a large deep neural network originally trained for speech recognition and further trains it on unlabeled speech data from a population that is similar to the target group, i.e., the elderly, in a self-supervised manner. The model is then fine-tuned and adapted for use across different datasets in multiple languages, including English, Italian, and Spanish. Evaluations on four publicly available PD datasets demonstrate the model's efficacy, achieving an average specificity of 92.1% and an average sensitivity of 91.2%. This method offers objective and consistent evaluations across large populations, addressing the variability inherent in human assessments and providing a non-invasive, cost-effective and accessible diagnostic option.

Problem

• Parkinson's Disease
• Speech Analysis
• Early Detection

Innovation

• Machine Learning
• Multilingual Speech Recognition
• Parkinson's Disease Detection