Automatic Assessment of Depression from Speech and Behavioural Signals

Research into automatic recognition and prediction of depression from behavioural signals like speech and facial video represents an exciting mix of opportunity and challenge. The opportunity comes from the huge prevalence of depression worldwide and the fact that clinicians already explicitly or implicitly account for observable behaviour in their assessments. The challenge comes from the multi-factorial nature of depression, and the complexity of behavioural signals, which convey several other important types of information as well as depression. Investigations in our group to date have revealed some interesting perspectives on how to deal with confounding effects (e.g. due to speaker identity) and the role of depression-related signal variability. This presentation will focus on how depression is manifested in the speech signal, how to model depression in speech, methods for mitigating unwanted variability in speech, how depression assessment is different from more mainstream affective computing, what is needed from depression databases, and different possible system designs and applications. A range of fertile areas for future research will be suggested.