Reference number : GC-2017-Q1-00231, amendment number : , agreement type : G, recipient legal name : Rudzicz, Frank (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Collaborative Health Research Projects, program name french : Projets de recherche concertée sur la santé, program purpose english : Assessing Alzheimer's disease (AD) is an expensive and laborious process that is_x000D_
unsustainable given our rapidly aging population, especially if one accounts for indirect costs_x000D_
such as lost time in travel, wait times, and hours spent in assessment, which is a process so_x000D_
labourious that it is often repeated only every few years. Moreover, the high variability of_x000D_
symptoms in AD cannot be ascertained accurately from a single assessment, which_x000D_
minimizes the effectiveness of current practice. Rapid, repeatable, and remote assessment is_x000D_
essential._x000D_
Many individuals with AD have diminished language abilities. Linguistic measures, like the_x000D_
ratio of pronouns to nouns, relate strongly to cognitive decline. By measuring such features_x000D_
and using advanced machine learning software, we have developed computational methods_x000D_
that are over 85% accurate in identifying Alzheimer's disease from short speech samples. As_x000D_
we continue to improve this technology, we will make it available over the public phone_x000D_
network accessible by a 1-800 number. This system will automatically receive calls from (and_x000D_
make calls to) outpatients, and completely automate speech-based tests including questionanswering,_x000D_
narratives, and random item generation. This will be encapsulated in an interface_x000D_
that healthcare workers can use to assign and monitor assessments and exercises._x000D_
Currently, to extract language measures, speech must be transcribed by professionals, which_x000D_
is impossible on large scales. Unfortunately, no automatic speech recognition (ASR) system_x000D_
exists for people with AD. We will therefore provide the first complete ASR system for people_x000D_
with AD._x000D_
Our objectives are: 1) to improve the accuracy of ASR for people with AD; 2) to refine and_x000D_
validate our software in remote monitoring/assessment; 3) to embed our software into a userfriendly_x000D_
phone-based interface., program purpose french : Assessing Alzheimer's disease (AD) is an expensive and laborious process that is_x000D_
unsustainable given our rapidly aging population, especially if one accounts for indirect costs_x000D_
such as lost time in travel, wait times, and hours spent in assessment, which is a process so_x000D_
labourious that it is often repeated only every few years. Moreover, the high variability of_x000D_
symptoms in AD cannot be ascertained accurately from a single assessment, which_x000D_
minimizes the effectiveness of current practice. Rapid, repeatable, and remote assessment is_x000D_
essential._x000D_
Many individuals with AD have diminished language abilities. Linguistic measures, like the_x000D_
ratio of pronouns to nouns, relate strongly to cognitive decline. By measuring such features_x000D_
and using advanced machine learning software, we have developed computational methods_x000D_
that are over 85% accurate in identifying Alzheimer's disease from short speech samples. As_x000D_
we continue to improve this technology, we will make it available over the public phone_x000D_
network accessible by a 1-800 number. This system will automatically receive calls from (and_x000D_
make calls to) outpatients, and completely automate speech-based tests including questionanswering,_x000D_
narratives, and random item generation. This will be encapsulated in an interface_x000D_
that healthcare workers can use to assign and monitor assessments and exercises._x000D_
Currently, to extract language measures, speech must be transcribed by professionals, which_x000D_
is impossible on large scales. Unfortunately, no automatic speech recognition (ASR) system_x000D_
exists for people with AD. We will therefore provide the first complete ASR system for people_x000D_
with AD._x000D_
Our objectives are: 1) to improve the accuracy of ASR for people with AD; 2) to refine and_x000D_
validate our software in remote monitoring/assessment; 3) to embed our software into a userfriendly_x000D_
phone-based interface., agreement title english : Automatic remote screening of speech features associated with Alzheimer's disease, agreement title french : Automatic remote screening of speech features associated with Alzheimer's disease, agreement number : CHRPJ, agreement value : 220132.0, agreement start date : 2017-04-25, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2020-2021), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2020-2021), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada