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，考虑到我们快速老龄化的人口，这是不可持续的，特别是如果考虑到间接成本，如在旅行中损失的时间，等待时间和花费在评估上的时间，这是一个非常耗时的过程，通常每隔几年才重复一次。此外，阿尔茨海默病症状的高度可变性不能从单一评估中准确确定，这降低了当前实践的有效性。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。语言学上的措施，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，从简短的语音样本中识别阿尔茨海默氏症的准确率超过85%。随着我们继续改进这项技术，我们将通过1-800号码在公共电话网络上提供这项技术。该系统将自动接听门诊病人的电话，并完全自动化基于语音的测试，包括问答、叙述和随机项目生成。这将被封装在一个界面中，卫生保健工作者可以使用它来分配和监测评估和练习。目前，为了提取语言度量，必须由专业人员对语音进行转录，这在大规模上是不可能的。不幸的是，没有针对AD患者的自动语音识别（ASR）系统。因此，我们将为AD患者提供第一个完整的ASR系统。我们的目标是：1)提高AD患者ASR的准确性；2)在远程监控/评估中完善和验证我们的软件；3)将我们的软件嵌入到用户友好的基于手机的界面中。

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.