Automatic Longitudinal Investigation of Multiple Sclerosis Subjects

Multiple Sclerosis is a chronic inflammatory disease of the central nervous system. Over time, people with MS may experience significant changes in cognition, language and speech processes. In this study we investigate speech utterances recorded over the course of three years for 16 MS subjects and 12 healthy controls. Our examination is based on speaker category classification (healthy or MS) using wav2vec2 embeddings as features. We found that subject classification performance improved over time: the 0.745-0.844 AUC values from year one increased to 0.891-0.979 in the third year. By analyzing the posterior estimates, we measured a statistically significant improvement in the scores corresponding to the third year for the MS category, while for the control subjects there was no such tendency. This, in our view, indicates that the change is due to a subtle deterioration in the condition of MS patients, which was detected by our machine learning workflow.