Speech analysis could provide an indicator of Alzheimer's disease and help develop clinical tools for automatically detecting and monitoring disease progression. While previous studies have employed acoustic (speech) features for characterisation of Alzheimer's dementia, these studies focused on a few common prosodic features, often in combination with lexical and syntactic features which require transcription. We present a detailed study of the predictive value of purely acoustic features automatically extracted from spontaneous speech for Alzheimer's dementia detection, from a computational paralinguistics perspective. The effectiveness of several state-of-the-art paralinguistic feature sets for Alzheimer's detection were assessed on a balanced sample of DementiaBank's Pitt spontaneous speech dataset, with patients matched by gender and age. The feature sets assessed were the extended Geneva minimalistic acoustic parameter set (eGeMAPS), the emobase feature set, the ComParE 2013 feature set, and new Multi-Resolution Cochleagram (MRCG) features. Furthermore, we introduce a new active data representation (ADR) method for feature extraction in Alzheimer's dementia recognition. Results show that classification models based solely on acoustic speech features extracted through our ADR method can achieve accuracy levels comparable to those achieved by models that employ higher-level language features. Analysis of the results suggests that all feature sets contribute information not captured by other feature sets. We show that while the eGeMAPS feature set provides slightly better accuracy than other feature sets individually (71.34%), “hard fusion” of feature sets improves accuracy to 78.70%.

中文翻译：

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在自发言语中检测阿尔茨海默氏痴呆症的副语言声学特征评估

语音分析可以提供阿尔茨海默病的指标，并有助于开发用于自动检测和监测疾病进展的临床工具。虽然之前的研究采用声学（语音）特征来表征阿尔茨海默氏症，但这些研究侧重于一些常见的韵律特征，通常与需要转录的词汇和句法特征相结合。我们从计算副语言学的角度详细研究了从自发语音中自动提取的纯声学特征对阿尔茨海默氏痴呆检测的预测价值。在 DementiaBank 的 Pitt 自发语音数据集的平衡样本上评估了几种最先进的副语言特征集对阿尔茨海默氏症检测的有效性，与性别和年龄相匹配的患者。评估的功能集是扩展的日内瓦简约声学参数集 (eGeMAPS)、emobase 功能集、ComParE 2013 功能集和新的多分辨率耳蜗图 (MRCG) 功能。此外，我们引入了一种新的主​​动数据表示（ADR）方法，用于阿尔茨海默氏痴呆识别中的特征提取。结果表明，仅基于通过我们的 ADR 方法提取的声学语音特征的分类模型可以达到与采用高级语言特征的模型所达到的准确度水平相当的准确度。结果分析表明，所有特征集都贡献了其他特征集未捕获的信息。我们表明，虽然 eGeMAPS 特征集提供的准确度略高于其他特征集（71.34%），