Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention
arXiv:2606.09271v1 Announce Type: cross Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria. As speech production relies on the precise coordination of complex neuromuscular mechanisms, speech analysis has emerged as a promising non-invasive and cost-effective biomarker for early PD detection. Recent deep learning approaches have shown encouraging results; however, most existing methods rely on a single speech representation, potentially overlooking complementary pathological information
The rapid advancements in deep learning and AI-driven speech analysis are converging with the increasing need for early, non-invasive diagnostic tools for neurodegenerative diseases.
This development represents a significant step towards enabling earlier detection and potentially better management of Parkinson's disease, reducing healthcare costs and improving patient outcomes.
The diagnostic landscape for Parkinson's disease is evolving to include more sophisticated, AI-driven, non-invasive speech analysis methods, moving beyond traditional clinical assessments.
- · AI healthcare tech companies
- · Patients with Parkinson's disease
- · Neurology departments
- · Biomarker developers
- · Traditional diagnostic methods
- · Healthcare systems with limited AI integration
Improved early detection rates for Parkinson's disease.
Increased demand for AI-powered diagnostic tools and data collection in clinical settings.
Potential for similar AI-driven non-invasive biomarkers for other neurological or systemic diseases.
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Read at arXiv cs.LG