Jekaterina Novikova is a Director of Machine Learning at Winterlight Labs. Winterlight Labs is a Toronto-based Canadian company that is developing a novel AI-based diagnostic platform that can objectively assess and monitor cognitive health. Jekaterina’s work explores artificial intelligence in the context of language understanding, characterizing speaker’s cognitive, acoustic and linguistic state, as well as in the context of human-machine interaction. Jekaterina received a PhD in Computer Science in 2015 from the University of Bath, UK. More information on Jekaterina’s research can be found at: http://jeknov.tumblr.com
Upcoming Abstract Summary
Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language
Alzheimer’s disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer’s, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer’s disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer’s detection performance the most, due to their impact on the features of syntactic and lexical complexity.