Computerized assessment of linguistic indicators of lucidity in Alzheimer's Disease dementia

拟议项目的重点是实现对突发事件的自动检测和分析， 在患有晚期痴呆症的个体中，该个体长期被认为已经屈服于 痴呆症和失去了他或她的大部分认知能力暂时恢复沟通的能力，在一个明确的 连贯的时尚。目前，这些事件存在的证据大多是轶事， 来自护理人员和医疗保健专业人员的报告。根据这些报告，清晰的言语和语言 是认知清醒期最显著的特征非常低的频率和意想不到的性质 这些事件的发生使得以音频或视频记录的形式捕获客观证据变得具有挑战性， 需要对这些事件进行系统和全面的调查。因此，有必要开发 自动语言分析的技术解决方案，可用于长期连续监测 老年痴呆症晚期的患者。在这个可行性项目中，我们将开发技术来解决两个问题， 具有挑战性的问题：a）连续语音到文本的准确转换，以及B）文本的自动分析 来衡量连贯性的程度。如果没有针对这些问题的强有力的解决方案， 在长期监测环境中捕获和分析连贯语音将仍然是有限的。我们将解决这些问题 通过开发和测试基于深度学习的强大自动语音识别解决方案， 可以自主运行的技术（无需向外部服务器发送数据）。我们还将调整现有的 并开发新的语义连贯性的措施，能够对不完美的成绩单造成的 自动语音识别为了开发和验证这些工具和方法，我们将使用现有的 轻度和中度痴呆症患者以及健康人的自发对话语音数据集 控制可作为卡罗莱纳对话收集和痴呆症银行的一部分。

The focus of the proposed project is to enable automated detection and analysis of episodes of unexpected lucidity in individuals with late-stage dementia in which the individual long thought to have succumbed to dementia and lost most of his or her cognitive abilities temporarily regains the ability to communicate in a clear and coherent fashion. Currently, the evidence for the existence of these episodes is mostly anecdotal, stemming from reports by caregivers and healthcare professionals. According to these reports, clear speech and language are the most prominent features of episodes of cognitive lucidity. The very low frequency and unexpected nature of these episodes make it challenging to capture objective evidence in the form of audio or video recordings of these events needed to enable systematic and comprehensive investigations. Thus, it is necessary to develop technological solutions for automated linguistic analysis that can be used for long-term continuous monitoring of individuals in late stages of dementia. In this feasibility project, we will develop technology to address two challenging issues: a) accurate conversion of continuous speech to text, and b) automated analysis of the text to measure the degree of coherence. Without robust solutions for these problems, our ability to detect and fully capture and analyze coherent speech in a long-term monitoring setting will remain limited. We will address these problems by developing and testing a robust automatic speech recognition solution based on deep learning technology that can operate autonomously (without sending data to external servers). We will also adapt existing and develop new measures of semantic coherence that are able to work on imperfect transcripts resulting from automatic speech recognition. In order to develop and validate these tools and approaches, we will use existing datasets of spontaneous conversational speech by persons with mild and moderate dementia as well as healthy controls available as part of the Carolina Conversations Collection and Dementia Bank.