Domain-aware Intermediate Pretraining for Dementia Detection with Limited Data

Youxiang Zhu, Xiaohui Liang, John A. Batsis, and Robert M. Roth

Detecting dementia using human speech is promising but faces a limited data challenge. While recent research has shown general pretrained models (e.g., BERT) can be applied to improve dementia detection, the pretrained model can hardly be fine-tuned with the available small dementia dataset as that would raise the overfitting problem. In this paper, we propose a domain-aware intermediate pretraining to enable a pretraining process using a domain-similar dataset that is selected by incorporating the knowledge from the dementia dataset. Specifically, we use pseudo-perplexity to find an effective pretraining dataset, and then propose dataset-level and sample-level domain-aware intermediate pretraining techniques. We further employ information units (IU) from previous dementia research and define an IU-pseudo-perplexity to reduce calculation complexity. We confirm the effectiveness of perplexity by showing a strong correlation between perplexity and accuracy using 9 datasets and models from the GLUE benchmark. We show that our domain-aware intermediate pretraining improves detection accuracy in almost all cases. Our results suggested that the difference in text-based perplexity values between patients with Alzheimer’s Disease and Healthy Control is still small, and the perplexity incorporating acoustic features (e.g., pause) may make the pretraining more effective.

Available soon!

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: