■语音分析有望作为早期发现阿尔茨海默病(AD)和轻度认知功能障碍(MCI)的筛查工具。声学特征和语言特征通常用于语音分析。然而,尚未有研究确定哪种类型的特征提供更好的筛查效果,特别是在中国大量的老龄化人口中。
■首先,为了比较声学特征的筛选效果,语言特征,以及使用相同数据集的组合。其次,使用从汉语母语者那里获得的自我收集的自然话语数据来开发汉语自动诊断模型。
■共有92名来自上海社区的参与者,在训练有素的操作员的指导下,完成了MoCA-B和基于Cookie盗窃的图片描述任务,分为三组,包括AD,MCI和基于MoCA-B评分的健康控制(HC)。声学特征(音高,抖动,微光,MFCC,Formants)和语言特征(词性,类型-令牌比率,信息词,信息单元)被提取。本研究中使用的机器算法包括逻辑回归,随机森林(RF),支持向量机(SVM),高斯朴素贝叶斯(GNB),和k-最近邻居(kNN)。使用声学特征的同一ML模型的验证精度,语言特征,并对其组合进行了比较。
■在训练中,语言特征的准确性通常高于声学特征。SVM区分HC和AD的最高准确率为80.77%,基于从语音数据中提取的所有特征,虽然区分HC和AD或MCI的最高准确度是通过RF实现的80.43%,仅基于语言特征。
■我们的结果表明,语言特征在认知障碍自动诊断中的实用性和有效性,并验证了中文数据自动诊断的适用性。
UNASSIGNED: Speech analysis has been expected to help as a screening tool for early detection of Alzheimer\'s disease (AD) and mild-cognitively impairment (MCI). Acoustic features and linguistic features are usually used in speech analysis. However, no studies have yet determined which type of features provides better screening effectiveness, especially in the large aging population of China.
UNASSIGNED: Firstly, to compare the screening effectiveness of acoustic features, linguistic features, and their combination using the same dataset. Secondly, to develop Chinese automated diagnosis model using self-collected natural discourse data obtained from native Chinese speakers.
UNASSIGNED: A total of 92 participants from communities in Shanghai, completed MoCA-B and a picture description task based on the Cookie Theft under the guidance of trained operators, and were divided into three groups including AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic features (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of-speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random forest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k-Nearest neighbor (kNN). The validation accuracies of the same ML model using acoustic features, linguistic features, and their combination were compared.
UNASSIGNED: The accuracy with linguistic features is generally higher than acoustic features in training. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features.
UNASSIGNED: Our results suggest the utility and validity of linguistic features in the automated diagnosis of cognitive impairment, and validated the applicability of automated diagnosis for Chinese language data.