Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection

TL;DR

This paper introduces novel high-level acoustic and linguistic features, along with a task-oriented approach, to improve speech-based Alzheimer's disease detection, demonstrating superior performance and potential for automated screening.

Contribution

It proposes a new method combining high-level features and task-related modeling for more accurate AD detection from speech data.

Findings

High-level features improve detection accuracy

Task-oriented modeling enhances semantic and syntactic cues

Audio-only methods show promising generalization

Abstract

With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants' description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task.