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.