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Speech datasets for identifying Alzheimer's disease (AD) are generallyrestricted to participants performing a single task, e.g. describing an imageshown to them. As a result, models trained on linguistic features derived fromsuch datasets may not be generalizable across tasks. Building on prior workdemonstrating that same-task data of healthy participants helps improve ADdetection on a single-task dataset of pathological speech, we augment anAD-specific dataset consisting of subjects describing a picture with multi-taskhealthy data. We demonstrate that normative data from multiple speech-basedtasks helps improve AD detection by up to 9%. Visualization of decisionboundaries reveals that models trained on a combination of structured picturedescriptions and unstructured conversational speech have the least out-of-taskerror and show the most potential to generalize to multiple tasks. We analyzethe impact of age of the added samples and if they affect fairness inclassification. We also provide explanations for a possible inductive biaseffect across tasks using model-agnostic feature anchors. This work highlightsthe need for heterogeneous datasets for encoding changes in multiple facets ofcognition and for developing a task-independent AD detection model.

The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech