MIT researchers have developed a neural-network model that can analyze raw text and audio data from interviews to discover speech patterns indicative of depression. This method could be used to develop diagnostic aids for clinicians that can detect signs of depression in natural conversation.

Neural network learns speech patterns that predict depression in clinical interviews.

Model can more naturally detect depression in conversations

MIT computer scientists have developed a system that learns to identify objects within an image, based on a spoken description of the image.  Image: Christine Daniloff