Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer's disease (AD) detection methods are characterized by their non-invasiveness, cost-effectiveness, and convenience. Previous studies have demonstrated the efficacy of fine-tuning pre-trained language models (PLMs) for AD detection. However, the objective of this traditional fine-tuning method, which involves inputting only transcripts, is inconsistent with the masked language modeling (MLM) task used during the pre-training phase of PLMs. In this paper, we investigate prompt-based fine-tuning of PLMs, converting the classification task into a MLM task by inserting prompt templates into the transcript inputs. We also explore the impact of incorporating pause information from forced alignment into manual transcripts. Additionally, we compare the performance of various automatic speech recognition (ASR) models and select the Whisper model to generate ASR-based transcripts for comparison with manual transcripts. Furthermore, majority voting and ensemble techniques are applied across different PLMs (BERT and RoBERTa) using different random seeds. Ultimately, we obtain maximum detection accuracy of 95.8% (with mean 87.9%, std 3.3%) using manual transcripts, achieving state-of-the-art performance for AD detection using only transcripts on the ADReSS test set.

翻译：相较于其他临床筛查技术，基于语音与语言的自动化阿尔茨海默病（AD）检测方法具有非侵入性、成本效益高及便捷性等优势。先前研究已证明通过微调预训练语言模型（PLMs）进行AD检测的有效性。然而，这种仅输入文本转录的传统微调方法，其目标与PLMs在预训练阶段采用的掩码语言建模（MLM）任务存在不一致性。本文研究了基于提示的PLMs微调方法，通过向转录文本输入中插入提示模板，将分类任务转化为MLM任务。同时，我们探讨了将强制对齐获得的停顿信息融入人工转录文本的影响。此外，我们比较了多种自动语音识别（ASR）模型的性能，并选用Whisper模型生成基于ASR的转录文本，以与人工转录文本进行对比。进一步地，我们在不同随机种子下对多种PLMs（BERT与RoBERTa）应用了多数投票与集成技术。最终，使用人工转录文本在ADReSS测试集上取得了95.8%的最高检测准确率（均值87.9%，标准差3.3%），实现了仅基于转录文本的AD检测任务的先进性能。