Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's

TL;DR

This study uses acoustic and linguistic features with machine learning to predict Alzheimer's disease severity, outperforming previous models and highlighting the importance of handcrafted linguistic features.

Contribution

It introduces a comparison of ten regression models using over 13,000 features, identifying the most significant handcrafted linguistic features for Alzheimer's prediction.

Findings

Handcrafted linguistic features are more significant than acoustic and learned features.

Selected features improve prediction accuracy over baseline models.

Top features are identified through recursive elimination and correlation methods.

Abstract

The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and track aging-associated disease in cognitive function among older adult populations is imperative. In particular, research related to automatic detection and evaluation of Alzheimer's disease (AD) is critical given the disease's prevalence and the cost of current methods. As AD impacts the acoustics of speech and vocabulary, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted and learned features that capture linguistic and acoustic phenomena. Using a subset of 54 top features selected by two methods: (1) recursive elimination and (2) correlation scores, we outperform a state-of-the-art baseline for the same task. Upon scoring and evaluating the statistical significance of each of the selected subset of features for each model, we find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.