Interpretability of self-supervised audio embeddings for clinical speech analysis

Determine which aspects of speech signals are captured by self-supervised audio embeddings and how those aspects relate to underlying clinical constructs, in order to establish their clinical interpretability and suitability for health applications.

Background

The paper discusses self-supervised learning and foundational models as sources of powerful audio embeddings for voice and speech analysis. Although these representations can encode salient acoustic information, they do not provide direct physiological or clinical interpretations. The authors note that embeddings may also capture confounding information, including recording-channel characteristics, language content, and sociodemographic biases, which can undermine their reliability in clinical applications.

The unresolved issue is therefore to identify the signal properties represented by these embeddings and to establish how those properties map onto clinically meaningful constructs. Resolving this issue would support the responsible use of embeddings alongside interpretable acoustic baselines and help determine whether their predictive performance reflects clinically relevant information rather than spurious correlations.