Automatic remote screening of speech features associated with Alzheimer's disease

评估阿尔茨海默病（AD）是一个昂贵而费力的过程，考虑到我们迅速老龄化的人口，这是不可持续的，特别是如果考虑到间接成本，如旅行时间损失，等待时间和评估时间，这是一个令人沮丧的过程，它通常每隔几年重复一次。此外，AD症状的高度变异性不能从一次评估中准确地确定，这使得目前的实践效果最小化。快速、可重复和远程评估是必不可少的。许多AD患者的语言能力下降。语言测量，如代词与名词的比例，与认知能力下降密切相关。通过测量这些特征并使用先进的机器学习软件，我们开发了计算方法，在从短语音样本中识别阿尔茨海默病方面的准确率超过85%。随着我们继续改进这项技术，我们将通过1-800号码访问的公共电话网络提供这项技术。该系统将自动接收来自门诊病人的电话，并完全自动化基于语音的测试，包括回答问题，叙述和随机项目生成。这将被封装在一个界面中，医护人员可以使用它来分配和监控评估和练习。目前，为了提取语言测量，必须由专业人员转录语音，这在大规模上是不可能的。不幸的是，没有自动语音识别（ASR）系统存在的人与AD。因此，我们将提供第一个完整的ASR系统的人与AD。我们的目标是：1）提高准确性的ASR的人与AD; 2）完善和验证我们的软件在远程监测/评估; 3）嵌入我们的软件到一个用户友好的手机为基础的界面。

Assessing Alzheimer's disease (AD) is an expensive and laborious process that isunsustainable given our rapidly aging population, especially if one accounts for indirect costssuch as lost time in travel, wait times, and hours spent in assessment, which is a process solabourious that it is often repeated only every few years. Moreover, the high variability ofsymptoms in AD cannot be ascertained accurately from a single assessment, whichminimizes the effectiveness of current practice. Rapid, repeatable, and remote assessment isessential.Many individuals with AD have diminished language abilities. Linguistic measures, like theratio of pronouns to nouns, relate strongly to cognitive decline. By measuring such featuresand using advanced machine learning software, we have developed computational methodsthat are over 85% accurate in identifying Alzheimer's disease from short speech samples. Aswe continue to improve this technology, we will make it available over the public phonenetwork accessible by a 1-800 number. This system will automatically receive calls from (andmake calls to) outpatients, and completely automate speech-based tests including questionanswering,narratives, and random item generation. This will be encapsulated in an interfacethat healthcare workers can use to assign and monitor assessments and exercises.Currently, to extract language measures, speech must be transcribed by professionals, whichis impossible on large scales. Unfortunately, no automatic speech recognition (ASR) systemexists for people with AD. We will therefore provide the first complete ASR system for peoplewith AD.Our objectives are: 1) to improve the accuracy of ASR for people with AD; 2) to refine andvalidate our software in remote monitoring/assessment; 3) to embed our software into a userfriendlyphone-based interface.