摘要
背景:即使在今天,阿尔茨海默病(AD)前驱阶段的可靠诊断依然是一个巨大的挑战。我们的研究主要集中在轻度认知功能障碍(MCI)认知功能下降的最早检测指标。由于即使在AD的轻度阶段也有语言障碍的报道,因此本研究的目的是开发一种灵敏的神经心理学筛选方法,该方法基于在执行记忆任务期间自发言语产生的分析。未来,这可以形成基于互联网的互动筛选软件的基础,以识别MCI。 方法:参与者为38名健康对照和48名临床诊断的MCI患者。引发自发言论,要求病人回忆两张黑白短片(一个是直接的,一个是延迟的),并回答一个问题。从记录的语音信号中首先手动(使用Praat软件)提取声学参数(犹豫比率,语速,长度和无声和充满暂停的数量,发声长度),然后自动地用自动语音识别(ASR )的工具。首先对提取的参数进行统计分析。然后,我们应用机器学习算法,根据声学特征来自动判别MCI和对照组。 结果:统计分析显示,大部分声学参数(发音速度,发音速率,无声暂停,犹豫比例,发音长度,每语停顿比率)存在显着差异。两组之间最显着的差异是在延迟回忆任务中的发言速度以及回答问题的停顿次数。分析过程的全自动化版本 - 即使用基于ASR的功能与机器学习相结合 - 能够以78.8%的F1分数分离两个类别。 结论:自发言语的时间分析可以用于实施一个新的,基于自动检测的工具,为社区筛选MCI。
关键词: 轻度认知障碍,自发言语,诊断,声学分析,时间特征,语音识别,机器学习。
Current Alzheimer Research
Title:A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech
Volume: 15 Issue: 2
关键词: 轻度认知障碍,自发言语,诊断,声学分析,时间特征,语音识别,机器学习。
摘要: Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI.
Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features.
Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%.
Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.
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Cite this article as:
A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech, Current Alzheimer Research 2018; 15 (2) . https://dx.doi.org/10.2174/1567205014666171121114930
DOI https://dx.doi.org/10.2174/1567205014666171121114930 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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