F. Bagheri

Self-supervised audio models can accurately detect pediatric asthma from breath sounds, achieving 84% accuracy in emergency settings where traditional diagnostics fall short.

Combining graph neural networks with self-supervised speech embeddings can significantly enhance ALS detection and progression prediction, achieving state-of-the-art results.