AI-Driven Voice Biomarker Classification of Voice Disorders
Analysis
This paper presents a novel hierarchical machine learning framework for classifying benign laryngeal voice disorders using acoustic features from sustained vowels. The approach, mirroring clinical workflows, offers a potentially scalable and non-invasive tool for early screening, diagnosis, and monitoring of vocal health. The use of interpretable acoustic biomarkers alongside deep learning techniques enhances transparency and clinical relevance. The study's focus on a clinically relevant problem and its demonstration of superior performance compared to existing methods make it a valuable contribution to the field.
Key Takeaways
- •Introduces a hierarchical machine learning framework for classifying benign laryngeal voice disorders.
- •Utilizes acoustic features from sustained vowels and mirrors clinical triage workflows.
- •Combines deep spectral representations with interpretable acoustic features.
- •Outperforms existing methods, highlighting the potential for scalable, non-invasive vocal health tools.
Reference
“The proposed system consistently outperformed flat multi-class classifiers and pre-trained self-supervised models.”