Early detection of Alzheimer’s disease symptoms using speech longitudinally

An early symptom of Alzheimer’s Disease is difficulty in remembering recent events. These trends are reflected in problems in language and patterns of speech. Speech patterns of an individual can hence be used to determine the trajectories of preclinical cognitive decline. The difference in the cognitive trends over subject groups, analyzed using speech data collected over a long period of time, can be used to detect Alzheimer’s even before it can be confirmed clinically.
With the help of machine learning models, this process can be automated completely by using automatic speech recognition systems to transcribe the speech followed by analysis of these transcripts. This project will explore machine learning- based strategies to automate the early AD diagnosis pipeline.

Faculty Supervisor:

Yang Xu


Aparna Balagopalan


WinterLight Labs Inc


Computer science


Medical devices




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