Alzheimer's Disease Detection via Large Pre-Trained Speech and Language Models: Taxonomy, Progress, and Open Challenges
Year: 2026
Type: preprint
Abstract: Alzheimer's Disease (AD) detection from spoken language has advanced considerably with the rise of large pre-trained speech and language models, including wav2vec 2.0, BERT, generative LLMs, and emerging audio-LLMs. These models often report higher performance on shared AD detection benchmarks than handcrafted-feature pipelines, while reducing the need for manual feature design. Yet their advantages remain difficult to interpret: pre-trained representations are largely a black box, benchmark gains are not always statistically robust, and performance can vary substantially across languages, elicitation protocols, and clinical task definitions.This survey proposes a taxonomy of how large pre-trained speech and language models have been used for AD detection and progression-related tasks. We define Large Pre-trained Models (LPMs) as Transformer-based models trained with self-supervised or weakly supervised objectives, distinguishing them from earlier non-Transformer pre-trained representations. We then organize the literature into six usage strategies: direct application, fine-tuning, transfer learning, multitask learning, multilingual modeling, and generative-LLM-based assessment. Across these categories, we synthesize evidence from shared benchmarks and recent studies to answer four questions:1) what taxonomy captures how LPMs are used for AD detection;2) what progress and advantages do LPMs offer for AD detection;3) what challenges and limitations do LPMs exhibit; and4) what future opportunities and open tasks they present.
Institution: University of Edinburgh
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7207799528