Direct versus intermediate multi-task transfer learning for dementia detection from unstructured conversations

Leveraging unstructured conversations for detecting early dementia may be possible through information transfer from more systematically constrained representations.To explore whether cross-domain (from semi-structured to unstructured) transfer learning improves dementia classification from conversational speech, we fine-tuned a BERT-family model using semi-structured narratives.We further fine-tuned on naturalistic conversations recorded in the home, but found that direct transfer from BERT to conversations was more effective for improving generalization.These findings show scope to directly leverage unstructured language samples for in-the-wild dementia detection.