Acoustic and Linguistic Features for Early Detection of Cognitive Deficits

用于早期检测认知缺陷的声学和语言特征

• 批准号: 403605461
• 负责人: Professor Dr. Johannes Schröder
• 金额: --
• 依托单位: Klinik für Allgemeine Psychiatrie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/403605461?language=en
• 关键词: Acoustic; Linguistic; Features; Early; Detection

The demographic development in Germany is accompanied by an increase in geriatric diseases. Their most common representative is dementia, a chronic progressive disease that is accompanied by loss of autonomy in all areas of life. As no curative therapy is known, early secondary prevention measures are of great importance. Current diagnostic procedures require a thorough examination by medical specialists which cannot be offered comprehensively for cost and time reasons. Scientific studies show that speech capacity is an important indicator of dementia. The aim of the proposed project ALMED is to identify and develop automatic methods that examine a person's speech capacity for prototypic indicators of cognitive deficits in real-time and fully automatically, and to present the results so that specialists can include them as an additional source of information when diagnosing cognitive deficits. The development and evaluation of a fully automated speech analysis method will be based on data and experience from the established interdisciplinary longitudinal study on adulthood and ageing (ILSE) in which over the course of 20 years medical, psychiatric and neuropsychological parameters as well as 10,000 hours of interviews were collected from more than 1,000 subjects. In the ALMED project, a scientifically sound selection of reliable, robust acoustic and linguistic features will be extracted from speech fully automatically and examined for its potential for the early detection of cognitive deficits, and appropriate forms of presenting this information will be found. Moreover, the transcripts of the interviews generated during the project will unlock a unique resource for geriatricians and gerontologists on the basis of which new scientific insights into the relationship between speech and dementia are to beexpected on a representative sample. The automated speechanalysis extends the potential of previous methods since it can take place both directly during interpersonal communication, and indirectly across great distances or at a later time. Furthermore, the automation provides a detailed analysis and evaluation of speech capability which is independent of the time resources of medical specialists. Thus, there is reason to hope that such automated support will in the future enable a cost-effective and widespread early detection of cognitive deficits. Therapy can consequently be offered to patients at a time when the disease can still be influenced, circumstances can be moderated and complications can be mitigated.

德国的人口发展伴随着老年病的增加。他们最常见的代表是痴呆症，这是一种慢性进行性疾病，伴随着生活各个领域的自主性丧失。由于没有治愈性疗法，早期二级预防措施非常重要。目前的诊断程序需要由医学专家进行彻底的检查，由于费用和时间的原因，不能全面地提供这种检查。科学研究表明，言语能力是痴呆症的重要指标。拟议项目ALMED的目的是确定和开发自动方法，以实时和全自动方式检查一个人的言语能力，以获得认知缺陷的原型指标，并提供结果，以便专家在诊断认知缺陷时可以将其作为额外的信息来源。全自动语音分析方法的开发和评价将基于已建立的关于成年和老龄化的跨学科纵向研究的数据和经验，在该研究中，在20年的时间里，从1 000多名受试者中收集了医学、精神病学和神经心理学参数以及10 000小时的访谈。在ALMED项目中，将完全自动地从语音中提取可靠、强大的声学和语言特征，并检查其早期检测认知缺陷的潜力，并找到呈现此信息的适当形式。此外，项目期间产生的访谈记录将为老年病学家和老年病学家提供一个独特的资源，在一个有代表性的样本上，我们可以期待对语言和痴呆症之间关系的新的科学见解。自动语音分析扩展了以前方法的潜力，因为它可以在人际交流中直接进行，也可以在很远的距离或稍后间接进行。此外，自动化提供了一个详细的分析和评估的语音能力是独立的医疗专家的时间资源。因此，有理由希望这种自动化支持将在未来实现具有成本效益和广泛的认知缺陷的早期检测。因此，可以在疾病仍然可以受到影响的时候向患者提供治疗，可以缓和情况并减轻并发症。