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

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