Speech Based Depression Detection

Speech-based depression detection uses machine learning to identify patterns in speech indicative of depressive symptoms, aiming for earlier and more accessible diagnosis. Current research focuses on developing robust and interpretable models, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating attention mechanisms to focus on relevant speech segments and fusing multiple acoustic features for improved accuracy. This field holds significant promise for improving mental health care by providing objective, scalable screening tools and potentially offering insights into the specific symptoms underlying depression diagnoses.