LLM-Anchored Paralinguistic Enrichment for Alzheimer's Disease Detection

2026-09-09

AI Summary

本文提出LAPE方法，通过整合语调事件文本化、词汇-语调单元化及基于文本的副语言融合，以增强LLM在阿尔茨海默病检测中的表现，利用言语特征提高识别准确性。

Abstract

Speech-based automatic detection of Alzheimer's disease (AD) provides a non-invasive and scalable approach to early cognitive screening. AD affects both lexical-semantic organization and speech production, including atypical pauses and word elongations. However, existing methods have yet to fully integrate these paralinguistic cues with linguistic content. We propose LLM-Anchored Paralinguistic Enrichment (LAPE), which enriches LLM-derived linguistic representations with paralinguistic cues through three coordinated innovations. The first is prosodic event textualization, which enables the LLM to model pauses and elongations jointly with lexical content by encoding them as explicit markers with bounded duration-aware repetition. The second is lexico-prosodic unitization and chunking, which preserves event identity and magnitude in both modalities by pooling only consecutive word units. The third is text-anchored paralinguistic fusion, which integrates local and utterance-level speech features by using NormGate to normalize and dynamically scale them relative to text. We evaluate LAPE on ADReSS and ADReSSo using participant-level cross-validation and leave-one-subject-out evaluation. LAPE achieves state-of-the-art performance across all four primary settings. Code will be released upon acceptance.

Problem

• Alzheimer's disease
• paralinguistic cues
• speech-based detection
• lexical-semantic organization
• speech production

Innovation

• LLM-Anchored Paralinguistic Enrichment
• Prosodic Event Textualization
• Lexico-prosodic Unitization
• Text-anchored Paralinguistic Fusion