Project
Voice and Language Biomarkers for Health
Detecting health symptoms from voice, language, and other sensor data.
Overall aim: To detect health symptoms from voice, language, and other sensor data.
Team members: Isaac Bevers, Kaley Jenny, Rahul Brito, Jordan Wilke, Miles Silva, Fabio Catania.
Prior work
Detecting vocal fold paralysis and biases in voice data collection
Analyzing sustained vowel and read speech to detect unilateral vocal fold paralysis. Also exploring biases in recording and comparing ML performance to clinicians’ ability to detect diagnosis from voice. Link to publication.
Collaborators: Phil Song.
Systematic review of studies detecting psychiatric disorders from speech
127 studies, 8 psychiatric disorders, description of acoustic features, guidelines for data acquisition and machine learning models. Link to publication.
Collaborators: Kate Bentley.
Discovering concerns and changes during COVID-19 on Reddit support groups
Analyzing 800,000+ posts from 2018 to 2021, we tracked changes in semantic content across 15 mental health support groups (e.g., r/SuicideWatch, r/BipolarReddit) and found vulnerable groups and a doubling of posts within suicidality and loneliness clusters. Link to publication.
Featured in MIT News and CNET.
Mental Health Reddit Dataset: Zenodo · GitHub & OSF.
Collaborators: Laurie Rumker, Tanya Talkar, John Torous, Guillermo Cecchi.
Estimating the effect of peer responses to suicidal posts among veterans
Collaborators: Matt Nock, Walter Dempsey, Kelly Zuromski, Daniel Kessler, RallyPoint.