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

arXiv: https://arxiv.org/abs/1811.12254 · HTML: https://mlsanity.com/paper/1811.12254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Comparing Acoustic-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2106.01555/) (2021)
- [Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text](https://mlsanity.com/paper/2109.11888/) (2021)
- [Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?](https://mlsanity.com/paper/2203.17110/) (2022)
- [Model-agnostic interpretation by visualization of feature perturbations](https://mlsanity.com/paper/2101.10502/) (2021)
- [Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's](https://mlsanity.com/paper/2209.06085/) (2022)

## Abstract (arXiv)

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.