CAtCh: Cognitive Assessment through Cookie Thief
Abstract
Several machine learning algorithms have been developed for the prediction of Alzheimer's disease and related dementia (ADRD) from spontaneous speech. However, none of these algorithms have been translated for the prediction of broader cognitive impairment (CI), which in some cases is a precursor and risk factor of ADRD. In this paper, we evaluated several speech-based open-source methods originally proposed for the prediction of ADRD, as well as methods from multimodal sentiment analysis for the task of predicting CI from patient audio recordings. Results demonstrated that multimodal methods outperformed unimodal ones for CI prediction, and that acoustics-based approaches performed better than linguistics-based ones. Specifically, interpretable acoustic features relating to affect and prosody were found to significantly outperform BERT-based linguistic features and interpretable linguistic features, respectively. All the code developed for this study is available at https://github.com/JTColonel/catch.
Cite
@article{arxiv.2506.06603,
title = {CAtCh: Cognitive Assessment through Cookie Thief},
author = {Joseph T Colonel and Carolyn Hagler and Guiselle Wismer and Laura Curtis and Jacqueline Becker and Juan Wisnivesky and Alex Federman and Gaurav Pandey},
journal= {arXiv preprint arXiv:2506.06603},
year = {2025}
}