DHB: Measuring Spoken Language Variability in Elderly Individuals

DHB：测量老年人口语变异性

• 批准号: 0826654
• 负责人: Steven Bedrick
• 金额: $ 75万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-11-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0826654&HistoricalAwards=false
• 关键词: DHB; Measuring; Spoken; Language; Variability

The focus of this project is to develop techniques to objectively (automatically) measure spoken language variability and change in aging. Many of the most effective methods for cognitive assessment are mediated by observed behavior, particularly spoken language production. These include clinical instruments, e.g., the Mini Mental Status Examination (MMSE), but also less formal assessments involving interviews or dialogs with physicians or even friends and family. Behavioral changes noted through these spoken language interactions could indicate pathological changes associated with a disorder; or the changes may be transient, due to missing medication or depression at the time of assessment. Alternatively, the observed behavior may be simply due to normal change in spoken language due to aging, or even within the range of natural behavioral variation. Understanding normal versus pathological language change with age requires the collection and annotation of repeated samples from both healthy and impaired individuals. This project has three specific aims: 1) to collect and transcribe longitudinal spoken language sample data elicited in multiple ways from diverse elderly adults; 2) to develop algorithms for automatically extracting features from these spoken language samples; and 3) to characterize the variability of feature values across samples of the same individual; and the utility of feature values and even feature variances for discriminating between subject groups. A particular challenge being addressed by this research is to achieve high-quality, efficient automatic annotation of discourse structure for the spoken language samples. The resulting methods are expected to directly contribute to important behavioral assessment applications.

该项目的重点是开发技术以（自动）衡量口语的可变性和衰老变化。 许多最有效的认知评估方法都是通过观察到的行为，尤其是口语生产来介导的。 其中包括临床工具，例如迷你心理状况考试（MMSE），但还涉及与医生甚至朋友和家人进行访谈或对话的正式评估。通过这些口语互动指出的行为变化可能表明与疾病相关的病理变化。或由于评估时缺少药物或抑郁症而变化可能是短暂的。或者，观察到的行为可能仅仅是由于衰老甚至在自然行为变化范围内的口语正常变化所致。 了解正常的与病理语言随着年龄的变化，需要收集和注释健康和受损个体的重复样本。 该项目具有三个具体的目的：1）收集和抄写纵向口语样本数据以多种方式从不同的老年人中获取； 2）开发用于自动从这些口语样本中提取功能的算法； 3）表征相同个体样本中特征值的变异性；以及特征值的实用性，甚至是特征差异，以区分主题组。 这项研究提出的一个特殊的挑战是为口语样本实现对话语结构的高质量，有效的自动注释。 所得的方法有望直接有助于重要的行为评估应用。