...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer speech and language >Identifying Mild Cognitive Impairment and mild Alzheimer's disease based on spontaneous speech using ASR and linguistic features
【24h】

Identifying Mild Cognitive Impairment and mild Alzheimer's disease based on spontaneous speech using ASR and linguistic features

机译：使用ASR和语言特征基于自发性语音识别轻度认知障碍和轻度阿尔茨海默氏病

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Alzheimer's disease (AD) is a neurodegenerative disorder that develops for years before clinical manifestation, while mild cognitive impairment is clinically considered as a prodromal stage of AD. For both types of neurodegenerative disorders, early diagnosis is crucial for the timely treatment and to decelerate progression. Unfortunately, the current diagnostic solutions are time-consuming. Here, we seek to exploit the observation that these illnesses frequently disturb the mental and linguistic functions, which might be detected from the spontaneous speech produced by the patient. First, we present an automatic speech recognition based procedure for the extraction of a special set of acoustic features. Second, we present a linguistic feature set that is extracted from the transcripts of the same speech signals. The usefulness of the two feature sets is evaluated via machine learning experiments, where our goal is not only to differentiate between the patients and the healthy control group, but also to tell apart Alzheimer's patients from those with mild cognitive impairment. Our results show that based on only the acoustic features, we are able to separate the various groups with accuracy scores between 74-82%. We attained similar accuracy scores when using only the linguistic features. With the combination of the two types of features, the accuracy scores rise to between 80-86%, and the corresponding F-1 values also fall between 78-86%. We hope that with the full automation of the processing chain, our method can serve as the basis of an automatic screening test in the future. (C) 2018 Elsevier Ltd. All rights reserved.
机译：阿尔茨海默氏病（AD）是一种神经退行性疾病，在临床表现之前已经发展了多年，而轻度认知障碍在临床上被认为是AD的前驱阶段。对于两种类型的神经退行性疾病，早期诊断对于及时治疗和减缓病情至关重要。不幸的是，当前的诊断解决方案非常耗时。在这里，我们试图利用以下观察结果：这些疾病经常会干扰心理和语言功能，而这可能是从患者发出的自发言语中发现的。首先，我们提出了一种基于自动语音识别的程序，用于提取一组特殊的声学特征。其次，我们提出一种语言特征集，该特征集是从相同语音信号的抄本中提取的。这两个功能集的有用性通过机器学习实验进行了评估，我们的目标不仅是要区分患者和健康对照组，还要区分阿尔茨海默氏症患者和轻度认知障碍患者。我们的结果表明，仅基于声学特征，我们就能够以74-82％的准确度分数将各个组分开。仅使用语言功能时，我们获得了相似的准确性得分。结合这两种类型的功能，精度得分将上升到80-86％之间，相应的F-1值也将下降到78-86％之间。我们希望，随着加工链的完全自动化，我们的方法将来可以作为自动筛选测试的基础。 （C）2018 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号