Machine Learning Algorithm Spots Depression in Speech Patterns
Researchers from the University of Southern California have developed a new machine learning tool capable of detecting certain speech-related diagnostic criteria in patients being evaluated for depression. Known as SimSensei, the tool listens to patient’s voices during diagnostic interviews for reductions in vowel expression characteristic of psychological and neurological disorders that may not be sufficiently clear to human interviewers. The idea is (of course) not to replace those interviewers, but to add additional objective weight to the diagnostic process. The group’s work is described in the journal IEEE Transactions on Affective Computing. Depression misdiagnosis is a huge problem in health care, particularly in cases in which a primary care doctor making (or not) the diagnosis. A 2009 meta-study covering some 50,000 patients found that docs were correctly identifying depression only…
Link to Full Article: Machine Learning Algorithm Spots Depression in Speech Patterns
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