Validating an automated speech assessment pipeline for use with individuals with amyotrophic lateral sclerosis (ALS)

Amyotrophic lateral sclerosis (ALS) is a devastating disease that affects many parts of patients’ lives, such as the abilities to think, move, and speak. Speech problems in ALS can have a substantial effect on patients’ quality of life. They are also related to faster disease progression and shorter survival time. It may be possible to detect the onset of ALS head and neck muscle problems early using speech recordings, which could improve planning of care for ALS patients. Speech recordings may also be useful measures for ALS clinical trials. Our proposed industry partner, Winterlight Labs, has developed an app that records speech and uses artificial intelligence (AI) to detect speech problems in a variety of diseases. In this study, we will investigate the ability of their app to detect speech problems in people with ALS. We will also determine if their app can distinguish people with ALS from healthy individuals. By conducting this study, we will gain a better understanding of speech problems related to ALS. Winterlight will benefit by gaining access to new data, which will help improve its app, and which could also make their app suitable as a tool to use in clinical trials of ALS treatments.

Faculty Supervisor:

Yana Yunusova

Student:

Partner:

WinterLight Labs Inc

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

University of Toronto

Program: