Analysis of spontaneous speech data elicited through Cookie Theft picture description (1-5 minute samples) from 240 people probable AD category and 233 healthy controls. Linguistic and acoustic variables used to train a machine learning classifier to distinguish between the groups, with 82% accuracy.
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer's Disease,49(2), 407-422.
A temporal Bayes network trained on 182 lexicosyntactic, 210 acoustic, and 85 semantic features extracted from 393 spontaneous speech samples elicited through Cookie Theft picture description can predict MMSE scores with a mean absolute error of 3.8, comparable to within-subject interrater (clinician) standard deviation of 3.9 to 4.8.
Yancheva, M., Fraser, K., & Rudzicz, F. (2015). Using Linguistic Features Longitudinally to Predict Clinical Scores for Alzheimer's Disease and Related Dementias.
Syntactic and semantic features were automatically extracted from transcriptions of narrative speech for three groups: semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. Machine learning classifiers trained on these features were able to distinguish between the three participant groups with up to 100% accuracy.
Fraser, K.C., Meltzer, J.A., Graham, N.L., Leonard, C., Hirst, G., Black, S.E., & Rochon, E. (2014). Automated classification of primary progressive aphasia subtypes from narrative speech transcripts. Cortex; a journal devoted to the study of the nervous system and behavior, 55, 43-60.
Frank Rudzicz is an international expert on speech technology for individuals with speech disorders. He is the President of the international joint ACL/ISCA special interest group on Speech and Language Processing for Assistive Technology, a Young Investigator of the Alzheimer’s Society a Scientist at Toronto Rehab, and an Assistant Professor at the University of Toronto.
Liam Kaufman has published peer reviewed articles in cognitive neurology, human computer interaction and neuroscience. He is an experienced software developer, and has successfully launched Understoodit, a startup acquired by EventMobi, and featured in The Toronto Star, CTV News, and TechCrunch.
Katie Fraser has published a number of peer-reviewed papers in both computer science conferences and neuroscience journals about her research on the automatic detection of dementia, and post-stroke aphasia. She was awarded a Google Canada Anita Borg scholarship and an NSERC scholarship and was named a 2015 MIT Rising Star in Computer Science.
Maria Yancheva conducts research on the automatic detection of dementia longitudinally, for which she was awarded an NSERC scholarship. She has experience developing software for Scotiabank’s trade floor, and has co-founded GroceryGo, a startup featured on BlogTO.
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