Clinical Report: Enhancing Detection of Respiratory Disorders via SSL-Optimized Acoustic Assessment

Overview

This study evaluates a screening model that integrates stress-induced acoustic analysis with machine learning to enhance the diagnostic sensitivity of vocal and respiratory signals.

Background

Respiratory diseases pose a significant global health challenge, often leading to persistent functional impairments. Traditional assessments may overlook subtle physiological changes that can be captured through voice analysis. This study focuses on Post-Acute Sequelae of SARS-CoV-2 (PASC).

Data Highlights

Key Findings

• Physical exertion significantly improved classification performance across all tasks.
• Fusion of acoustic features with SSL embeddings achieved peak F1-scores of 82.2% for vowel phonation and 80.8% for coughing.
• A cross-task late fusion model aggregation reached the highest overall performance with an F1-score of 87.7%.
• Self-Supervised Learning representations enhance the sensitivity of voice-based screening.
• Post-exercise measurements improve the robustness and consistency of classification.

Clinical Implications

The integration of self-supervised learning in acoustic analysis may provide a framework for detecting respiratory and vocal sequelae.

Conclusion

Incorporating advanced machine learning techniques into voice analysis enhances diagnostic sensitivity for respiratory disorders.

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