An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer's Dementia in Spontaneous Speech
An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer's Dementia in Spontaneous Speech
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DOI：
10.1109/jstsp.2019.2955022
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发表时间：
2020-02-01
影响因子：
7.5
通讯作者：
Luz, Saturnino
中科院分区：
文献类型：
--
作者：
Haider, Fasih;de la Fuente, Sofia;Luz, Saturnino
语音分析可以提供阿尔茨海默病的指标，并帮助开发自动检测和监测疾病进展的临床工具。虽然以前的研究已经采用声学（语音）特征的阿尔茨海默氏症的特点，这些研究集中在一些常见的韵律特征，往往结合需要转录的词汇和句法特征。我们提出了一个详细的研究预测价值的纯声学特征自动提取的自发语音阿尔茨海默氏症的痴呆症检测，从计算语言学的角度来看。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阿尔茨海默氏症检测语言特征集的有效性，患者按性别和年龄匹配。评估的特征集为扩展日内瓦最小声学参数集（eGeMAPS）、emobase特征集、ComparE 2013特征集和新的多分辨率相干图（MRCG）特征。此外，我们介绍了一种新的主动数据表示（ADR）的阿尔茨海默氏痴呆症识别的特征提取方法。结果表明，仅基于声学语音特征提取的分类模型，通过我们的ADR方法可以实现的精度水平相比，采用更高级别的语言功能的模型所实现的。结果分析表明，所有功能集的贡献信息不被其他功能集。我们表明，虽然eGeMAPS功能集提供了比其他功能集略好的准确性（71.34%），“硬融合”的功能集提高准确性到78.70%。
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%.