Exploring ASR-Based Wav2Vec2 for Automated Speech Disorder Assessment: Insights and Analysis

AI models for medical speech assessment can work well but remain poorly understood: The Wav2Vec2 model achieved strong results in assessing speech disorders in head and neck cancer patients, but researchers found it functioned as a "black box" with no clear explanation of how the model's decisions connected to actual clinical assessments—highlighting a critical gap between performance and interpretability in medical AI applications.

Interpretability methods can help demystify medical AI systems: The researchers used explainability techniques like Canonical Correlation Analysis and visualization methods to better understand which parts of the model were most important for clinical decision-making, demonstrating that post-hoc analysis can make opaque AI systems more transparent for healthcare professionals.

Not all layers of AI models contribute equally to medical decisions: Through layer-wise analysis, the study identified that specific layers within the Wav2Vec2 model were more critical for assessing speech intelligibility and severity, suggesting that understanding model architecture is essential before deploying AI in clinical settings.

Pre-training data choices matter for medical applications: The researchers compared different versions of the Wav2Vec2 model based on their training data sources, indicating that how and where an AI model learns initially affects its ability to handle specialized medical tasks—an important consideration when selecting or developing AI tools for healthcare.