DECT：利用LLM辅助的细粒度语言知识与标签切换和标签保留数据生成进行阿尔茨海默病诊断
DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease
AI总结：
DECT利用LLM进行细粒度语言分析和标签切换/保留数据生成，以增强AD语音检测，在DementiaBank上准确率提升11%。
摘要：
阿尔茨海默病（AD）是一种不可逆的神经退行性疾病，影响全球5000万人。低成本、准确地识别AD的关键标志物对于及时诊断和干预至关重要。语言障碍是认知衰退的最早迹象之一，可用于区分AD患者与正常对照个体。患者与访谈者之间的对话可用于检测此类障碍，但这些对话往往混杂有模糊、嘈杂和不相关的信息，使得AD检测任务变得困难。此外，AD语音样本的有限可用性及其语音风格的变异性对开发稳健的基于语音的AD检测模型构成了重大挑战。为解决这些挑战，我们提出了DECT，一种新颖的基于语音的领域特定方法，利用大型语言模型（LLM）进行细粒度语言分析和标签切换标签保留数据生成。我们的研究呈现了四个新颖之处：我们利用LLM的总结能力从嘈杂的语音转录中识别和提炼关键的认知-语言信息，有效过滤不相关信息。我们利用LLM固有的语言知识从非结构化和异构的音频转录中提取语言标记。我们利用LLM的组合能力生成包含多种语言模式的AD语音转录，以克服语音数据稀缺的挑战并增强AD检测模型的稳健性。我们使用增强的AD文本语音转录数据集和更细粒度的AD文本语音转录数据表示来微调AD检测模型。结果表明，与基线相比，DECT在DementiaBank数据集上的AD检测准确率提高了11%，展现了优越的模型性能。
英文摘要：
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease affecting 50 million people worldwide. Low-cost, accurate identification of key markers of AD is crucial for timely diagnosis and intervention. Language impairment is one of the earliest signs of cognitive decline, which can be used to discriminate AD patients from normal control individuals. Patient-interviewer dialogues may be used to detect such impairments, but they are often mixed with ambiguous, noisy, and irrelevant information, making the AD detection task difficult. Moreover, the limited availability of AD speech samples and variability in their speech styles pose significant challenges in developing robust speech-based AD detection models. To address these challenges, we propose DECT, a novel speech-based domain-specific approach leveraging large language models (LLMs) for fine-grained linguistic analysis and label-switched label-preserved data generation. Our study presents four novelties: We harness the summarizing capabilities of LLMs to identify and distill key Cognitive-Linguistic information from noisy speech transcripts, effectively filtering irrelevant information. We leverage the inherent linguistic knowledge of LLMs to extract linguistic markers from unstructured and heterogeneous audio transcripts. We exploit the compositional ability of LLMs to generate AD speech transcripts consisting of diverse linguistic patterns to overcome the speech data scarcity challenge and enhance the robustness of AD detection models. We use the augmented AD textual speech transcript dataset and a more fine-grained representation of AD textual speech transcript data to fine-tune the AD detection model. The results have shown that DECT demonstrates superior model performance with an 11% improvement in AD detection accuracy on the datasets from DementiaBank compared to the baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
