Augmented Adversarial Self-Supervised Learning for Early-Stage Alzheimer's Speech Detection

The early-stage detection of Alzheimer's disease has been considered an important field of medical studies. While speech-based automatic detection methods have raised attention in the community, traditional machine learning methods suffer from data shortage because Alzheimer's record data is very difficult to get from medical institutions. To address this problem, this study proposes an augmented adversarial self-supervised learning method for Alzheimer's disease detection using limited speech data. In our approach, Alzheimer-like patterns are captured through an augmented adversarial self-supervised framework, which is trained in an adversarial manner using limited Alzheimer's data with a large scale of easily-collected normal speech data and an augmented set of Alzheimer's data. Experimental results show that our model can effectively handle the data sparsity problems and outperform the several baselines by a large margin. The performance for the AD class has been improved significantly, which is very important to actual AD detection applications.

Automated summary

Early-stage detection of Alzheimer's disease is crucial, but traditional machine learning methods face challenges due to data scarcity. This study proposes an augmented adversarial self-supervised learning approach for Alzheimer's detection, effectively addressing data sparsity and outperforming other methods.