Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models

TL;DR

This paper introduces a reasoning-based method combining speech recognition and large language models with Chain-of-Thought prompting to improve Alzheimer's detection accuracy, achieving state-of-the-art results.

Contribution

It presents a novel CoT reasoning approach integrating speech and language models for Alzheimer's diagnosis, with significant performance improvements over prior methods.

Findings

16.7% relative performance improvement with CoT reasoning

Achieved state-of-the-art results in Alzheimer's detection

Effective integration of speech recognition and LLMs

Abstract

Societies worldwide are rapidly entering a super-aged era, making elderly health a pressing concern. The aging population is increasing the burden on national economies and households. Dementia cases are rising significantly with this demographic shift. Recent research using voice-based models and large language models (LLM) offers new possibilities for dementia diagnosis and treatment. Our Chain-of-Thought (CoT) reasoning method combines speech and language models. The process starts with automatic speech recognition to convert speech to text. We add a linear layer to an LLM for Alzheimer's disease (AD) and non-AD classification, using supervised fine-tuning (SFT) with CoT reasoning and cues. This approach showed an 16.7% relative performance improvement compared to methods without CoT prompt reasoning. To the best of our knowledge, our proposed method achieved state-of-the-art performance in CoT approaches.