将说话停顿信息融入语言模型，提升阿尔茨海默病早期检测准确率。
Integrating Pause Information with Word Embeddings in Language Models for Alzheimer's Disease Detection from Spontaneous Speech
- 将停顿时长编码为嵌入向量，融合进Transformer模型
- 在ADReSSo数据集上达83.1%准确率，优于现有方法
- 适合语音分析、临床辅助诊断方向的研究者
阿尔茨海默病（AD）是一种进行性神经退行性疾病，以认知衰退和记忆丧失为特征。早期检测对干预和治疗至关重要。本文提出一种从自发性言语中检测AD的新方法，将停顿信息融入语言模型。通过将停顿信息编码为嵌入向量并集成至基于Transformer的语言模型中，使模型能够捕捉语音的语义与时间特征。我们在阿尔茨海默病自发言语识别数据集（ADReSS）及其扩展版ADReSSo上进行了实验，与现有方法对比。所提方法在ADReSSo测试集上达到83.1%的准确率。结果表明该方法能有效区分患者与健康人，凸显了停顿作为AD检测关键指标的潜力。本研究利用语音分析这一无创、低成本手段，为疾病早期诊断和管理提供了支持。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. Early detection of AD is crucial for effective intervention and treatment. In this paper, we propose a novel approach to AD detection from spontaneous speech, which incorporates pause information into language models. Our method involves encoding pause information into embeddings and integrating them into the typical transformer-based language model, enabling it to capture both semantic and temporal features of speech data. We conduct experiments on the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) dataset and its extension, the ADReSSo dataset, comparing our method with existing approaches. Our method achieves an accuracy of 83.1% in the ADReSSo test set. The results demonstrate the effectiveness of our approach in discriminating between AD patients and healthy individuals, highlighting the potential of pauses as a valuable indicator for AD detection. By leveraging speech analysis as a non-invasive and cost-effective tool for AD detection, our research contributes to early diagnosis and improved management of this debilitating disease.
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