New algorithm could help diagnose depression by analyzing the tone of your voice

The concept of an AI psychoanalyst has been in circulation for decades, tracing all the way back to Joseph Weizenbaum’s ELIZA chatterbot in the 1970s. But now researchers from the University of Southern California are taking the idea to the next level, courtesy of a machine learning algorithm designed to analyze a person’s speech patterns and help diagnose the possibility of depression in the process. The tool is part of an ongoing research project called SimSensei, referring to a Kinect-powered virtual therapist able to “read” patient’s’ body language for signs of anxiety, nervousness, contemplation and other emotional attributes. Related: Machine learning could help revolutionize early Alzheimer’s diagnosis More recently, however, the project has increasingly focused on not just understanding the responses given (like Apple’s Siri does, for instance), but also the manner in which…


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