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 immediately, helping to better detect children at risk of internalizing disorders.
Computerized speech analysis provides a more accurate classification than clinical ratings for teens at risk of developing psychosis, a new study reports.
Researchers report computer based analysis of speech transcripts from teens at risk of psychosis were able to predict, with 83% accuracy, who would develop symptoms within 2 years.