Model can more naturally detect depression in conversations: Neural network learns speech patterns that predict depression in clinical interviews -- ScienceDailyOne key insight from the research, Alhanai notes, is that, during experiments, the model needed much more data to predict depression from audio than text. With text, the model can accurately detect depression using an average of seven question-answer sequences. With audio, the model needed around 30 sequences. "That implies that the patterns in words people use that are predictive of depression happen in shorter time span in text than in audio," Alhanai says. Such insights could help the MIT researchers, and others, further refine their models.