• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>EURASIP journal on audio, speech, and music processing >Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech
【24h】

Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech

机译：评估土耳其对话语音中检测阿尔茨海默氏病的语言和韵律特征

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic diagnosis and monitoring of Alzheimer’s disease can have a significant impact on society as well as the well-being of patients. The part of the brain cortex that processes language abilities is one of the earliest parts to be affected by the disease. Therefore, detection of Alzheimer’s disease using speech-based features is gaining increasing attention. Here, we investigated an extensive set of features based on speech prosody as well as linguistic features derived from transcriptions of Turkish conversations with subjects with and without Alzheimer’s disease. Unlike most standardized tests that focus on memory recall or structured conversations, spontaneous unstructured conversations are conducted with the subjects in informal settings. Age-, education-, and gender-controlled experiments are performed to eliminate the effects of those three variables. Experimental results show that the proposed features extracted from the speech signal can be used to discriminate between the control group and the patients with Alzheimer’s disease. Prosodic features performed significantly better than the linguistic features. Classification accuracy over 80% was obtained with three of the prosodic features, but experiments with feature fusion did not further improve the classification performance. Keywords Alzheimer’s disease Speech processing Linguistic features Prosodic features Machine learning
机译：自动诊断和监测阿尔茨海默氏病会对社会以及患者的健康产生重大影响。大脑皮层处理语言能力的部分是最早受该病影响的部分之一。因此，使用基于语音的功能来检测阿尔茨海默氏病越来越受到关注。在这里，我们研究了基于语音韵律的广泛功能集以及从土耳其与有或没有阿尔茨海默氏病患者的对话中转录而来的语言特征。与大多数专注于记忆回忆或结构化对话的标准化测试不同，自发的非结构化对话是在非正式环境下与受试者进行的。进行年龄，教育和性别控制的实验以消除这三个变量的影响。实验结果表明，从语音信号中提取的拟议特征可用于区分对照组和阿尔茨海默氏病患者。韵律特征的表现明显优于语言特征。使用三个韵律特征获得了超过80％的分类精度，但是使用特征融合的实验并没有进一步提高分类性能。关键词阿尔茨海默氏病语音处理语言特征韵律特征机器学习

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