A study of acoustic features for depression detection

Clinical depression can be considered as a soft biometric trait that can help to characterize an individual. This mood disorder can be involved in forensic psychological assessment, due to its relevance in different legal issues. The automatic detection of depressed speech has been object of research in the last years, resulting in different algorithmic approaches and acoustic features. Due to the use of different algorithms, databases and performance measures, deciding which ones are more suitable for this task is difficult. In this work, the performance of different acoustic features for depression detection was explored in a common framework. To do so, a depression estimation approach in which the audio data is segmented and projected into a total variability subspace was used, and these projected data was used to estimate the depression level by performing support vector regression. The data and evaluation metrics were the ones used in the audiovisual emotion challenge (AVEC 2013).