Acoustic and Linguistic Features for Early Detection of Cognitive Deficits

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，伴随着生活中所有领域的自主性丧失。由于目前尚无根治方法，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检查，但由于成本和时间原因，无法全面提供。科学研究表明，言语能力是痴呆症的重要指标。拟议的ALMED项目的目的是识别和开发自动方法，以实时和全自动地检查一个人的言语能力，寻找认知缺陷的原型指标，并展示结果，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估将基于已建立的关于成人和老龄化的跨学科纵向研究(ILSE)的数据和经验，在该研究中，20年来收集了1,000多名受试者的医学、精神病学和神经心理学参数以及10,000个小时的访谈。在Almed项目中，将全自动地从语音中提取可靠、健壮的声学和语言特征的科学选择，并检查其早期发现认知缺陷的潜力，并将找到适当的形式来呈现这种信息。此外，该项目期间产生的采访记录将为老年病学家和老年病学家打开一个独特的资源，在此基础上，有望在具有代表性的样本上对言语和痴呆症之间的关系提出新的科学见解。自动语音分析扩展了以前方法的潜力，因为它既可以在人际交流期间直接进行，也可以间接地跨越很远的距离或在稍后的时间进行。此外，自动化提供了与医疗专家的时间资源无关的语音能力的详细分析和评估。因此，我们有理由希望，这种自动化支持在未来将能够实现具有成本效益的、广泛的认知缺陷早期检测。因此，在疾病仍有可能受到影响、情况可以缓和、并发症可以减轻的时候，可以向患者提供治疗。

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.