Solving Real-life Challenges in Detecting Cognitive Diseases from Speech using ML
Solving Real-life Challenges in Detecting Cognitive Diseases from Speech using ML

Abstract: 

Machine learning (ML) has great potential in detecting cognitive, mental and functional health disorders from speech, as acoustic properties of speech and corresponding patterns in language are modified by a wide variety of health-related effects. However, there exist many real-life problems that make developing ML models a challenging task. In this talk, I will discuss some of the common challenges researchers are dealing with when developing ML models, such as the lack of appropriate training data, absence of labels, effects of automatic speech recognition, limitations of English-only models and the lack of interpretability. I will explain how these challenges affect the quality and performance of existing ML models used to detect cognitive diseases from speech and will present novel solutions we are developing at Winterlight Labs to deal with them.

Bio: 

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 conducted research in machine learning and natural language processing in both academia and industry for more than 7 years and has a strong track record of publications in top-level conferences. She received a PhD in Computer Science from the University of Bath, UK. Jekaterina’s work explores AI in the context of language understanding, characterizing speaker’s cognitive, acoustic and linguistic state, as well as in the context of human-machine interaction. In 2018, she was recognized by Re-Work as one of 30 influential women advancing AI in Canada. More information on Jekaterina’s research can be found at: http://jeknov.tumblr.com