用提示词和停顿信息提升语音诊断阿尔茨海默病的准确率
Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection
- 用提示词模板将分类任务转为语言模型预训练任务
- 结合语音停顿信息，手动转录文本达95.8%准确率
- 适合关注医疗语音分析与大模型应用的研究者
相比其他临床筛查手段，基于语音与语言的阿尔茨海默病（AD）自动化检测具有无创、低成本、便捷等优势。以往研究证明微调预训练语言模型（PLM）在AD检测中有效，但传统微调仅输入文本，与PLM预训练时使用的掩码语言建模（MLM）任务不一致。本文探索基于提示的PLM微调方法，通过在文本输入中插入提示模板，将分类任务转化为MLM任务。同时研究强制对齐获得的停顿信息对人工转录文本的影响。此外，对比多种自动语音识别（ASR）模型，选用Whisper生成基于ASR的转录文本，并与人工转录对比。还采用多数投票和集成学习策略，融合不同随机种子下的BERT与RoBERTa模型。最终，在ADReSS测试集上仅使用文本实现最高95.8%的检测准确率（均值87.9%，标准差3.3%），达到当前仅依赖文本的最优水平。
原文摘要 · Abstract (English)
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.
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