Comparator Loss: An Ordinal Contrastive Loss to Derive a Severity Score for Speech-based Health Monitoring (2509.17661v1)

Abstract: Monitoring the progression of neurodegenerative disease has important applications in the planning of treatment and the evaluation of future medications. Whereas much of the state-of-the-art in health monitoring from speech has been focused on classifying patients versus healthy controls, or predicting real-world health metrics, we propose here a novel measure of disease progression: the severity score. This score is derived from a model trained to minimize what we call the comparator loss. The comparator loss ensures scores follow an ordering relation, which can be based on diagnosis, clinically annotated scores, or simply the chronological order of the recordings. In addition to giving a more detailed picture than a simple discrete classification, the proposed comparator loss-based system has the potential to incorporate information from disparate health metrics, which is critical for making full use of small health-related datasets. We evaluated our proposed models based on their ability to affirmatively track the progression of patients with motor neuron disease (MND), the correlation of their output with clinical annotations such as ALSFRS-R, as well as their ability to distinguish between subjects with MND and healthy controls.