Linguistic cues for automatic assessment of Alzheimer’s disease across languages

Abstract

Background

Most common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.

Objective

This study explores the potential of a speech-based machine learning (ML) approach in estimating cognitive impairment, using inputs of speech audio recordings.

Methods

We develop an automatic ML pipeline that ingests multimodal inputs of audio and transcribed text, mapping speech and language to domain-specific biomarkers optimized for high explainability and predictive ability. The resulting features are fed through a multi-stage pipeline to determine efficient classification configurations.

Results

We evaluated the system on large real-world datasets, achieving above 90% and 70% weighted average F1 scores for two-class (AD versus normal controls) and three-class (AD versus mild cognitive impairment versus normal controls) classification tasks, respectively. Model performance remains stable across different population characteristics.

Conclusions

The study introduces a robust, non-invasive method for gauging the cognitive status of AD and MCI patients from speech samples, with the potential of generalizing effectively to multiple types of diseases/disorders which may burden language.