Bio

Katie Fraser is a PhD candidate at the University of Toronto in the Computational Linguistics group, where her main research interests are text processing, automatic speech recognition, and machine learning. She is particularly interested in how these techniques can be used to assess potential cognitive impairment. She received a Master of Computer Science degree from Dalhousie University in Halifax, Nova Scotia, where she developed techniques for reducing noise and blur in microscope images. Before that, she researched the structure and dynamics of glass-forming liquids as part of her Bachelor of Science in Physics at St. Francis Xavier University in Antigonish, Nova Scotia.

Text and speech processing for the detection of dementia

Text and speech processing for the detection of dementia

It has been shown that language can be a sensitive barometer of cognitive health. However, current approaches to screening and diagnosis for dementia do not typically include a detailed analysis of spontaneous speech because the manual annotation of language samples is far too time-consuming. Using methods from natural language processing and machine learning, we have been able to extract relevant linguistic and acoustic features from short speech samples and their transcripts to predict whether the speaker has Alzheimer’s Disease with 92% accuracy. We have also investigated a type of dementia called primary progressive aphasia (PPA), in which language ability is the primary impairment. In addition to determining whether participants had PPA or not, we were able to distinguish between semantic-variant PPA and agrammatic-variant PPA by incorporating features to detect signs of empty speech and syntactic simplification. Another component of my current work involves improving automatic speech recognition for cognitive assessment. By developing computational tools to collect, analyze, and interpret language data from cognitively impaired speakers, I hope to provide the groundwork for numerous potential applications, including remote screening, support for diagnosis, assistive technologies for community living, and the quantitative evaluation of therapeutic interventions.