Abstract
Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived from Whisper ASR transcripts on the ADReSSo 2021 diagnosis dataset. We evaluate interpretable machine-learning models, including Logistic Regression and Linear Support Vector Machines, using TF-IDF text representations under repeated 5x5 stratified cross-validation. Our results demonstrate that transcript quality has a statistically significant impact on classification performance. Models trained on Whisper-small transcripts consistently outperform those using Whisper-base transcripts, achieving balanced accuracy above 0.7850 with Linear SVM. Paired statistical testing confirms that the observed improvements are significant. Importantly, classifier complexity contributes less to performance variation than ASR transcription quality. Feature analysis reveals that cognitively normal speakers produce more semantically precise object- and scene-descriptive language, whereas Alzheimer's speech is characterized by vagueness, discourse markers, and increased hesitation patterns. These findings suggest that high-quality ASR can enable simple, interpretable lexical models to achieve competitive Alzheimer's detection performance without explicit acoustic modeling. The study provides a reproducible benchmark pipeline and highlights ASR selection as a critical modeling decision in clinical speech-based artificial intelligence systems.
Abstract (translated)
从自发语音中早期检测阿尔茨海默病已成为一种有前景的非侵入性筛查方法。然而，自动语音识别（ASR）质量对下游临床语言建模的影响仍缺乏充分理解。本研究基于ADReSSo 2021诊断数据集，利用Whisper ASR转写文本衍生的词汇特征进行阿尔茨海默病检测研究。我们采用TF-IDF文本表示，在重复5×5分层交叉验证框架下评估可解释机器学习模型（包括逻辑回归与线性支持向量机）。结果表明，转写质量对分类性能具有统计显著影响：基于Whisper-small转写文本训练的模型持续优于Whisper-base版本，其中线性支持向量机达到0.7850以上的平衡准确率。配对统计检验证实观察到的改进具有显著性。重要的是，分类器复杂度对性能变异的影响小于ASR转写质量。特征分析显示，认知正常者产出更多语义精确的物体与场景描述性语言，而阿尔茨海默病语音则表现为模糊表达、话语标记及犹豫模式增加。这些发现表明，高质量ASR可使简单的可解释词汇模型无需显式声学建模即可实现具有竞争力的阿尔茨海默病检测性能。本研究提供了可复现的基准流程，并强调ASR选择是临床语音人工智能系统的关键建模决策。
URL
https://arxiv.org/abs/2603.18239
