The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
This work addresses the need for more robust and task-independent AD detection models, which is incremental as it builds on prior methods by incorporating heterogeneous data.
The paper tackled the problem of limited generalizability in Alzheimer's disease detection models trained on single-task speech data by augmenting an AD-specific dataset with multi-task healthy speech data, resulting in up to a 9% improvement in detection accuracy and reduced out-of-task error.
Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, models trained on linguistic features derived from such datasets may not be generalizable across tasks. Building on prior work demonstrating that same-task data of healthy participants helps improve AD detection on a single-task dataset of pathological speech, we augment an AD-specific dataset consisting of subjects describing a picture with multi-task healthy data. We demonstrate that normative data from multiple speech-based tasks helps improve AD detection by up to 9%. Visualization of decision boundaries reveals that models trained on a combination of structured picture descriptions and unstructured conversational speech have the least out-of-task error and show the most potential to generalize to multiple tasks. We analyze the impact of age of the added samples and if they affect fairness in classification. We also provide explanations for a possible inductive bias effect across tasks using model-agnostic feature anchors. This work highlights the need for heterogeneous datasets for encoding changes in multiple facets of cognition and for developing a task-independent AD detection model.