Neuroscience News -
6 May 2019 22:09
An auditory-based machine learning algorithm was able to identify children diagnosed with depression and anxiety with 80% accuracy after analyzing recordings of their speech. The algorithm identified eight audio features that signify a higher risk of depression. Of these, a lower pitch of voice, repeatable speech inflections and a higher pitch response to surprise stimuli, were more indicative of depression. Researchers hope to develop a smartphone app that records and analyzes speech immediatel...
Share this Article