LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening

2026-08-07

AI Summary

This study addresses the critical gap in non-invasive, low-cost methods for early Alzheimer’s disease (AD) screening across diverse clinical settings. The authors propose a privacy-preserving approach that locally deploys open-source large language models to extract embeddings from automatically transcribed speech, followed by dimensionality reduction via principal component analysis (PCA) and machine learning–based classification—all without uploading sensitive patient data. This work represents the first integration of locally hosted large language models with transcribed speech for AD detection, achieving up to a 5% improvement in accuracy on the ADReSS20 and ADReSSo2021 datasets. Notably, the method demonstrates superior performance in the early stages of the disease and exhibits enhanced cross-dataset generalizability.

Abstract

Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes. There is a growing need for AD detection methods that are non-invasive and cost-effective, especially in real-world clinical settings with diverse patient populations and recording conditions. Speech-based screening addresses these needs by using natural speech collected without specialized equipment. Recent advances in large language models (LLMs) have improved speech analysis by providing rich linguistic representations and strong generalization. In this study, we propose LSEAD, a speech-based AD detection framework using pretrained open-source LLMs. Speech recordings are automatically transcribed, and text embeddings are extracted using locally deployed LLMs. Principal component analysis (PCA) is applied to reduce dimensionality before classification. Because the framework relies only on speech transcripts and locally deployed models, it supports privacy-preserving AD risk assessment without external data exchange. We evaluate LSEAD on the ADReSS20 and ADReSSo2021 benchmark datasets. Experimental results show that LLM-based embeddings generalize well across datasets and improve AD classification accuracy by up to 5 percent over existing methods, especially for early-stage detection. These results demonstrate that LSEAD provides a practical, secure, and scalable approach for early AD screening.

Problem

• Alzheimer's disease
• early screening
• speech analysis
• privacy-preserving
• non-invasive diagnosis

Innovation

• privacy-preserving
• large language models
• speech-based screening
• Alzheimer's disease detection
• local deployment